Proceedings of the 5th International Conference on Building Energy and Environment

Due to the lack of scientific construction standards and management systems, most of historic residence are in poor conditions. Therefore, advanced technologies seem necessary to maintain and protect these cultural heritages with low intervention, high efficiency, and high accuracy. Digital twin is such a technology constructed to integrate multi-source data to a digital platform based on Building Information Modeling (BIM), Internet of Things (IoT), cloud computing and other technologies. This report introduces a “4 M” (Modeling–Monitoring–Mining–Management) technology roadmap to build a dynamic visualized maintenance and management platform. Through the practice in a demonstration project in Fujian, China, this digital platform has been recognized by predicting several potential risks.

The estimation of the cooling power considerably influences the electrical energy consumption. Thus, numerical methods for its definition are crucial for the design of HVAC systems. In this study, it was investigate the variability of cooling power in residential buildings using different combinations of algorithms available in the E+ library to calculate indoor/outdoor convection and energy balance. Parametric and non-parametric statistical tests was performed to evaluate significant differences between the analyzed combinations. There is at least one significant difference between the [simple-adaptive-convection] and [simple-tarp] groups. The choice of algorithm can significantly influence the definition of artificial air conditioning systems. Thus, the present work points out the weight of choosing different combinations of algorithms in estimating the cooling heat load as a way to help in making decisions about the simulation input settings.

Due to historic value, and multi-needs from occupants, traditional optimization proposals are not applicable for historic residence. Hence, a systematic occupant-centric assessment method of the intervention level should be put forward. This paper introduces a data-driven evaluation scheme by considering the following dimensions, comfort level, structure safety, and spatial value. The Analytic Hierarchy Process (AHP) is employed to analyze the weight of relevant factors. A Chinese traditional residential dwelling in Fujian Province, are used as the reference building. The time-series data and static data are obtained by digital twin technology. By comprehensive considering the weights and the assessment results of three factors, the Intervention Level Index (ILI) distribution map is formed to classify the residence plan to three levels. The results show that most living areas’ ILI are over 3, which means optimizing strategy should focuses more on living areas in Wufu town based on the system. This result receives over 90% recognition from the occupants.

As a passive cooling strategy without energy consumption, radiative cooling has attracted considerable attentions. Building energy simulations have been conducted to identify the benefits of this technology. However, in existing studies, constant emissivity was used in the building energy simulation programs, which can cause certain errors. To tackle this problem, this study developed a method to couple the spectral-dependent radiative cooling with building energy simulations in EnergyPlus. The radiative cooling power calculated by the proposed spectral-dependent model can be significantly different from that by the existing constant-emissivity model. However, since the energy saving from radiative cooling was relatively small compared with the total energy consumption, the differences in energy consumption calculated by both models were not significant. The coupling of spectral-dependent radiative cooling with building energy simulation would improve the accuracy of energy performance assessment for buildings with radiative cooling technology.

Pollutant dispersion is of great relevance for people living in urban areas. Idealized point and line sources are commonly used to reproduce traffic emissions in street canyons. However, a limited number of studies focus on the usage of realistic sources, such as real car geometries, which can influence the flow characteristic thus the pollutant distribution inside the canyon. This is the goal of the present paper for which Computational Fluid Dynamics (CFD) simulations were performed on a street canyon to investigate the impact of an ideal point and a realistic source. At the first stage, CFD simulations were performed with an ideal point source and validated with wind-tunnel (WT) data. At the second stage, CFD simulations were carried out on a street canyon using a realistic source. The result shows that the distribution of V*, K*, and C* inside the canyon can be strongly affected by the presence of the car body.

Understanding and quantifying the interactions between urban microclimate and urban buildings is essential to improve urban environment and building performance. Previous studies used tool-or-application-specific data exchange mechanisms which cannot be generalized and adopted for other tools or applications. In this paper, we introduce a new flexible and tool agnostic data schema to facilitate the exchange of data between urban building energy models and urban microclimate models. The JSON schema was tested using a district of 97 buildings in San Francisco and running simulations with CityBES and CityFFD. Results show the data schema and coupling simulation technique work for the tested district. Compared with simulation results using the historical weather data, simulation results over a two-day heatwave event showed a 5.3 °C higher peak building facade average temperature and a 19.5% higher peak cooling energy use when the simulated microclimate data is used which considers the building heat emissions.

Fast fluid dynamics (FFD) could provide efficient airflow and concentration simulation. The commonly used turbulence model in FFD was RNG k- $$\varepsilon $$ ε turbulence model which solved two transport equations to obtain eddy viscosity. To improve computing speed, this investigation implemented no turbulence model, Smagorinsky model and dynamic Smagorinsky model which calculated eddy viscosity without solving equation in FFD in an open-source program, OpenFOAM. By simulating single-building case and comparing with experiment and CFD, this study assessed accuracy and efficiency of FFD with those turbulence models. Compared with CFD, FFD improved computing speed without reducing accuracy. The simulation of FFD without turbulence model was fast but inaccurate. FFD with Smagorinsky model increased computing speed while ensuring the same accuracy as RNG k- $$\varepsilon $$ ε turbulence model. FFD with dynamic Smagorinsky model provided accurate results with high efficiency. This investigation suggested FFD with dynamic Smagorinsky model for outdoor airflow and pollutant dispersion studies.

This study aims to examine the effects of building configuration on outdoor thermal comfort near an isolated building in a hot and humid climate. Tested building configuration included equivalent polygons, irregular quadrangles, slab-like (or V-shaped) models, and an arc. PET was adopted for predicting thermal comfort. Wind velocity data were acquired based on validated CFD simulations and heat-related parameters referred to published field surveys. Configuration parameters influencing thermal comfort are identified. They are the number of equilateral polygonal sides, building’s aspect ratio, included angle of slab-like buildings, and surface curvature. This study only focuses on plan configuration, whereas frontal configurations (e.g., tapered, titled, and helical) also deserve careful study. Using favorable building configurations to improve thermal comfort has significant practical implications, especially for high-density cities where land resources are in shortage. Architects and city planners are encouraged to consider beneficial configurations (e.g., small building’s aspect ratio) in their designs.

Windows are considered the least-efficient components in the thermal envelope driven by their low thermal resistance and static transmittance to solar gains. Thermochromic coatings are promising to address the latter. Nevertheless, current building energy simulation (BES) tools were not originally developed for the performance assessment of adaptive facades. Hence, in this paper, a previously developed 1-D transient heat transfer model was utilized to evaluate the annual heating and cooling energy performance of two thermochromic glazing systems, including commercial Ligand-Exchange (LET) and industrial Vanadium Dioxide (VO2) thermochromic coatings, and compare their performance to reference low-E coated glazing in the cold climate of Toronto, ON. Contrary to current BES, the model accounts for spectral-selectivity, gradual transmittance change, hysteresis, and switching time of thermochromic coatings. A representative room’s annual energy performance was evaluated for different glazing configurations and several exposures. The results show that the VO2 thermochromic glazing is the most energy-efficient configuration in Toronto, driven by low solar transmittance and increased near-infrared reflectance at higher coating temperatures. Furthermore, increasing the switching time and hysteresis width have less significant effects for VO2 glazing.

Recent studies have shown the importance of healthy, occupant-centric lighting in buildings, but urban apartment housing remains understudied. This project analyzed typical dwellings in apartment housing to determine how unit design parameters impacted daylight and healthy lighting. The simulation-based study compared results using two early-stage design tools: ALFA and ClimateStudio. Design options were evaluated to see whether they met either or all of certain lighting criteria for LEED, WELL, and Toronto Green Standard (TGS). Typically constructed, single aspect, 1-bedroom units were tested in four orientations with different balcony types. A main finding is that the three standards evaluate lighting in different ways. A design that meets LEED requirements may not necessarily meet WELL lighting requirements. As expected, unit orientation, geometry and aspect ratio were the most impactful parameters. Conclusions and recommendations are presented, and future work should focus on actual conditions on site, and surveys to evaluate resident preferences.

Architectural design problems usually depend on many performance indicators. The computational developments in the last decades allowed researchers to perform evaluations that comprise those indicators in Simulation-based Optimization (SBO) processes. However, studies still need to address optimization configurations, such as the right algorithms’ choice. This study proposes a comparison between two high performative algorithms, RBFMOpt and HypE, to determine which one is the most affected by the number of fitness functions. For the problem, we proposed a single-family house with eight design variables and fitness functions that vary from two to five. We used hypervolume, variability, IGD+ , GD+ , and EPS+ as metrics to point out the algorithm with the best performance. The results showed that HypE and RBFMOpt achieved a high performance. However, RBFMOpt requires a low budget and showed less variability between multiple runs. HypE have the best outcomes regarding the hypervolume indicator, but requires a larger evaluation budget.

Fast and accurate simulation of outdoor airflow distributions is crucial for studying urban microclimate. In this paper, three pressure-correction schemes (i.e., NIPC, SIPC and NSPF) are implemented in OpenFOAM and validated with experimental data. The differences of these schemes applying five k-ε turbulence models to quickly simulate the airflow around a single1:1:2 bluff body are analysed. These schemes can accurately predict the main airflow characteristics around the bluff body and are about 2.5–3.5 times faster than the PISO algorithm. The shortest computation time occurs when applying the RKE turbulence model, followed by SQKE and RNG, while the computation time is longest when applying SKE and LBKE. NIPC and SIPC have similar computational speeds, while NSPF is about 10–16% faster than them. Considering computational accuracy and efficiency, the RNG and SQKE are the best among the tested turbulence models.

People spend about 90% of their time indoors. Thus, seeking design strategies to address the challenge of indoor environmental quality (IEQ) at the design stage is crucial. However, few simulation studies have considered the mutual effect of all four components of IEQ. This paper aims to introduce a simulation-based framework involving parametric modeling to simultaneously quantify the impact of office design decisions on all four domains of IEQ namely, thermal comfort, visual comfort, acoustic comfort, and air quality. To achieve this goal, it first develops a set of metrics to quantify four IEQ domains. Then, the design parameters which impact any IEQ criteria in the design stage including room geometry, façade design, and material properties are provided. Finally, a case study is considered and investigated to demonstrate the proposed workflow and provide a set of near-optimal design parameters that yield desirable performance according to the indoor comfort metrics.

The importance of cockpit environment is generally overlooked, and detailed indoor related assessment is still limited in the existing literature. In this study, four numerical simulations were conducted using a Boeing 737 cockpit to investigate its air quality and thermal distribution. The thermal sensation of the pilots was assessed by comparing in-flight conditions (i.e. with and without window heat). Also, various indoor air-related indices were utilised to evaluate the ventilation performance and contaminant (CO2) removal under three ventilation configurations. The results demonstrated that window heat effect could significantly increase overall temperature (2 °C) and could lead to a severe thermal stratification from the pilots’ chest level. Current ventilation outcomes revealed that the air quality in upper region of the domain was not optimum, especially from pilots’ breathing level. The local contaminant removal in the human micro-environment was still ineffective even the personal gaspers were utilised. Potential health problems may occur.

Ventilation in urban climate is important for human comfort, and needs to be considered during the whole design process especially in the high-density urban environment. However, ventilation simulation requires a sharp learning curve and high cost of hardware. This study provides a tool for architects and urban designers to investigate ventilation condition without high demand of prerequisite CFD knowledge and computation hardware. This study uses urban surrogate modelling and CFD to construct a ventilation dataset, and uses this dataset to train a conditional generative adversarial neural network. The proposed program requires a master plan image as input, and provides immediate feedback on ventilation conditions within the area of interest. A 1500 m2 region in west coast of San Francisco is used as the case study. Results show that the generated ventilation simulation can provide a guidance on the ventilation condition with average accuracy of 80%. The proposed scheme relaxes the criteria for investigating ventilation in urban scale for design and assessment, provides application potential for fast ventilation visualization for architects and urban designers, and integration with UTCI for evaluation of human comfort in micro and meso climate.

In Oman, air conditioning is required in public classrooms to ensure thermally comfortable conditions for users, considering the harsh hot climate. In general, air conditioning is provided by conventional mechanical systems, such as split units that consume a considerable amount of energy. Besides the environmental consequences, this practice takes a significant portion of the governmental budget, as public-school operation cost is funded by the government. Increasing the thermostat setting combined with raising air velocity is promising in maintaining thermally comfortable classrooms with reduced cooling energy. Increasing air velocity is possible by ventilation systems, including the stratum system, where thermal comfort is achieved by directing the airstream toward the upper part of the human body. This paper reports the findings of numerical simulations using STAR CCM+ , a Computational Fluid Dynamics (CFD) software. A numerical model of a typical classroom was generated in STAR CCM+ and was then validated using experimental data conducted for an actual classroom. The validated model was utilized to explore the effect of applying 15 ventilation scenarios, including a stratum ventilation system. The results showed that the conventional air conditioning systems achieved thermal comfort at several spots within the domain, but have a risk of the thermal draft. On the other hand, the stratum ventilation system achieved better thermal comfort at relatively higher air temperature compared to the conventional air conditioning systems. The results inferred that the stratum system might need lower energy consumption than the traditional cooling system.

The present paper introduces a new hybrid RANS/LES turbulence model for urban flows CFD simulations called Flow-Based Stress-Blended Eddy Simulation (Fb-SBES). The blending from RANS to LES is performed on-the-fly, based on a numerical parameter that quantifies the deviation of the flow to the atmospheric boundary layer (ABL) conditions. The hybrid framework allows to solve the RANS equation in the major portion of the domain (88% in average for a single building) and perform LES in the proximity of the build environment. The RANS closure is an advanced $$k - \varepsilon$$ k - ε turbulence model that is tuned to be consistent with the neutral ABL conditions. Fb-SBES provides a notable improvement for the prediction of the mean streamwise velocity component compared to the advanced RANS model but also compared to a DDES.

Solid desiccant dehumidification can achieve efficient moisture control in air-conditioning systems. A small-scale dehumidifier can quickly regulate the moisture load of a specific space, such as drug storage, cultural relics preservation, etc. Although many mathematical models have been proposed to predict the performance of desiccant-coated air-conditioning systems, only a few of them can be applied to such a small-scale device. And many existing models take advantage of the empirical correlations to calculate the heat and mass transfer coefficients, which are infeasible to new desiccants and new system design and its optimization. In this work, we used the CFD method to investigate thermal and moisture transport performance on a small-scale Metal-organic Framework (MOF) based heat sink (SDCHS). The model built up the relationship among humid air, desiccant, and heat sink and then coupled the subdomains of the fluid flow, heat transfer, and specie transport. The reliability of this model was validated by the experimental tests of SDCHS under different operation conditions. Based on the measured results of humidity ratio and temperature, the discrepancies were maintained below 15%. A parametric optimization was subsequently carried out on the flow field of SDCHS to enhance the heat and mass transfer, and various performance indexes were calculated to demonstrate the maximum utilization efficiency under the given operating condition. This dimensional analysis provides some simple guidelines to the design of SDCHS with the most suitable geometry.

Essential needs such as electricity generation, water distribution, and water treatment account for 12.6% of US energy consumption, of which water distribution (3.15%) is highly energy-intensive with the average energy use of 1300 kilowatt-hours per million gallons (kWh/MG). Water Distribution Networks (WDNs) are promising candidates for providing demand response due to the large fluid inertias, pressurized piping networks, and high energy intensity associated with pumping. However, to take advantage of the demand response potential of WDNs, we need to better understand the operation of community-level water networks and ways of energy optimization in connection with electricity operation. In this paper, we develop component and system models of community-level WDN using equation-based object-oriented Modelica language. Further, we exhibit the water-energy interdependencies through demand response (DR) pump controls based on time-of-use and critical-peak energy pricing as well as the commonly used tank level-based pump control using the developed modeling package. The DR pump controls exhibit a 25–29% energy savings and 17–27% cost savings compared to the commonly used pump control. This research has the potential to support dynamic modeling and optimization, demand response, resiliency analysis, and integrated decision-making in future smart and connected communities.

Tap water has a potential for space cooling, providing free cooling during heatwaves without significant capital and operating costs. As a cooling medium, tap water with a 12 and 15 °C water inlet temperature was investigated. A copper pipe with a 10 cm spacing and a length of 58 m embedded in a masonry wall was selected. Two operation modes were modelled: (1) stationary mode where water is not flowing for the simulation period, and (2) flowing mode where water flows for 16 h. Preliminary simulations indicated that the maximum cooling capacity for the masonry wall was 76 W/m2. The maximum capacity dropped to 64 W/m2 after increasing the water inlet temperature to 15 °C. Maximum cooling energy of 8.11 kWh was delivered to room air and wall with 1200 L of water per day.

This study aims to present that the thermal properties of transparent building envelopes, e.g. U-factor and SHGC, need to be treated as dynamic variables. The aforementioned thermal properties have been treated in a deterministic fashion in many simulation studies. To quantify parameter uncertainty caused by U-factor and SHGC, the authors conducted uncertainty and sensitivity analysis for eight different window systems in 17 climate zones. The thermal properties are calculated using “pyWinCalc,” a python module using WINDOW calc engine developed by LBNL. When the dynamic properties are applied in a stochastic manner to a DOE reference medium-size office model, up to 13% of difference is found in monthly averaged heat flux flowing into the indoor space through windows, compared to a prediction out of the deterministic properties.

An urban flow solver requires accurate real-time simulations for operating urban air mobility (UAM) and improving the urban environment. This research presents a building-resolved large eddy simulation (LES) urban wind simulator based on multi-GPU compute unified device architecture (CUDA) computing, highlighting two aspects: the scalability of the calculation using multiple GPUs and regional wind flow patterns affecting the pedestrian-level wind environment and UAM in urban areas of Korea. Detailed analysis of urban wind flow interactions with weather conditions will be presented in the meeting.

This paper shows an innovative Test Laboratory built at the Mediterranea University of Reggio Calabria: TCLab of Building Future Lab with standardized and experimental tests on the envelope's technological and material assets. The reasons are to test the new performance of envelopes linked to the environmental variables of the contexts. The laboratory is designed to continuously update the instrumentation to carry out activities related to innovation trends that create intelligent, adaptive and integrated facades/buildings. Production is moving towards advanced innovation that makes the envelope due to better modelling, testing and customization. Hence the importance of experimentation of adaptive envelopes in response to stress phenomena increasingly present in the urban environment through innovative methods. We also intend to present as results some experiments and contents of the Horizon 2020 MetaBuildings Lab project.

Aiming at the uneven distribution of thermal environment in the building and the phenomenon of temperature deviation between the end sensor and the thermal environment in the work area, this paper takes an office in the north as the research object, and carries out simulation and experimental research on the indoor temperature distribution characteristics and its influencing factors. Firstly, a model was constructed using ANSYS, considering factors such as office building envelope, indoor thermal disturbance and outdoor meteorology, and the model was verified through experiments with an error of less than 2%. Secondly, based on the simulation and experimental data, the influence of each factor is analysed, and a fitting formula for the temperature deviation between the temperature sensor and the working area are proposed, laying a theoretical foundation for the indoor temperature end control technology.

With the growing trends of building decarbonization, electrified systems, e.g., Ground Source Heat Pump (GSHP) systems, have been attracting more attention than ever. One of the most significant barriers faced by GSHP systems is the high initial cost that limits the market volume from expanding, especially in the residential sector of the U.S. This study entails research work to develop a high-efficiency and low-cost GSHP system using vertical underground boreholes integrated with a dry fluid cooler for a typical single-family house. It is expected to provide an alternative option that leads to cost savings and increases GSHP’s market penetration in the U.S. The designed system model was established in the TRNSYS environment, and its feasibility, when deployed in a cold climate region, is verified after model calibration. The results show that the proposed heat pump system with the use of vertical boreholes and a dry fluid cooler, as well as the optimized control strategies to select when the vertical boreholes and/or the dry fluid cooler are used, is suitable for use in cold climate regions, and it can achieve similar or better efficiency for space heating compared to a conventional GSHP system at lower initial and/or operating costs.

Accurate building cooling load prediction can effectively guide the start-stop strategies and capacities matching of chillers and is also the basis of model predictive control of the heating, ventilation, and air conditioning (HVAC). Most of the existing literature focused on the structural optimization or selection of cooling load prediction models, and rarely in-depth studies on the matching between data and models. However, the data features determine the upper limit of model prediction performances, thus leading the unsatisfactory prediction accuracy in the existing methods. Aiming at this, the paper proposed a novel weight clustering-based pattern recognition method for improving building cooling load prediction reliability. Firstly, after the outliers were removed, the Pearson correlation analysis was used to select the key input variables for the models. Secondly, the sensitivity analysis was utilized to obtain the weights of input variables on the cooling load, and then the weights were introduced into the K-means clustering algorithm. Finally, the training data of models were classified by the clustering, and the corresponding training set was matched according to the predicted sample’s features. The case study showed that the weight clustering-based pattern recognition method has a significant improvement in prediction accuracies to the multiple linear regression (MLR), multiple nonlinear regression (MNR), and artificial neural network (ANN) models (e.g., 35%, 36%, and 15% reduction in mean absolute percentage error (MAPE), respectively), In addition, the optimal clustering number, the clustering effects with or without the weights, etc. were also investigated. This paper’s method can provide a novel idea for the models’ data preprocessing.

In the past decades, summer heatwaves are becoming more frequent and creating a major concern about their impacts on human health. Predicting outdoor thermal comfort during summer heatwaves is thus essential to help avoid economic and life losses. Existing research often investigates urban outdoor thermal comfort via CFD (computational fluid dynamics) modeling and indoor overheating conditions by BES (building energy simulations). However, modeling a large urban area under transient weather would require extensive computational costs and detailed building information settings. In the present work, a novel method coupling the CityFFD (City Fast Fluid Dynamics) and CityBEM (City Building Energy Model) is utilized to predict the air temperature distribution in three different urban areas during a summer heatwave in Montreal, Canada. The numerical model is well validated with onsite measurement data of the heatwave from July 15 to July 16, 2013. Based on the air temperature profile at the pedestrian level, the outdoor thermal comfort, and its impact were evaluated via Humidex. The results are also compared with the predicted urban thermal environment by Environment Canada. This paper shows that urban configurations may significantly affect the thermal risk during the summertime heatwave, and the proposed integrated model can estimate urban microclimate with details for urban overheating assessment.

We report in this paper the thermal comfort and energy saving potential of a decoupled ceiling radiant cooling panel (DCRCP), which is an infrared transparent and high resistance layer to separate cooling surface and air-contact surface. A CFD model was built for a DCRCP when it was installed inside a thermal chamber. Firstly, the thermal comfort was investigated when the DCRCP used different radiant panel surface temperatures. Results indicated that the thermal comfort of the DCRCP satisfied the PMV and PPD criteria of the ASHRAE Standard55-2017. Secondly, the energy saving potential of the DCRCP was studied using the same thermal chamber when inter walls were given different emissivity. Compared with commonly used wall emissivity, the energy saving potential of the DCRCP achieved ~ 8.4% as the emissivity is low as 0.1. It was suggested that low radiant panel surface temperature and low wall emissivity could improve the energy saving potential.

In recent years, the use of air-conditioners in high-rise residential buildings has significantly increased in tropical region due to the global warming and heat island effects. In high-rise buildings, the condenser units of the split-type air conditioners (A/C) are installed at the building re-entrants and heat dissipated from these units tends to create a heat column as a semi-enclosed stack along the re-entrant due to thermal buoyancy. This heat column generation has a significant impact on the energy performance of A/C units. Hence, in this study, CFD simulations were used to study the effect of alternative arrangements of the condenser units within the existing re-entrant parameters of a selected high-rise residential building in Colombo region to reduce the impacts of heat column generation. The simulation results showed that changing the distance to the condenser location from the exterior wall can significantly reduce the heat column generation effects.

Wind-driven rain (WDR) causes negative consequences such as degradation of surface material, frost damage, salt efflorescence, structural cracking, interior damage, etc. WDR has been studied extensively through experimental measurements, numerical simulations, and semi-empirical methods. The previous WDR studies, can be categorized into two areas: the study of WDR impinged on buildings, and the investigation of surface reactions to the impinging raindrops. The present study synthesizes previous studies’ results with regard to the characteristics of WDR impinged on façade, such as the main wetting pattern on various building configurations, i.e., stand-alone (isolated), street canyon, building-array, urban area. A comprehensive summary of the effect of meteorological and geometrical parameters concerning WDR impinged on buildings is provided. It is identified that wind speed and direction, as well as building configuration, are found to be the most significant meteorological and geometric parameters, respectively.

Due to the aging and lower adaptability of urban traditional community infrastructure, many community parks face abandonment. However, its landscape renewal can positively influence urban sustainability construction and climate resilience. This study aims to reproduce the current and updated cases by comparing planting structures integrated with four landscape conditions in a typical Japanese community park. This study used ENVI-met microclimate simulations combined with the Predicted Mean Vote (PMV) thermal index to assess updated cases adapted to local summer climate traits. The results indicated that the presence of a water body and green roof had a lower PMV distribution with a 1.2 °C air temperature reduction in the same landscape planting layout and no vegetation. Moreover, the tree-grass structure has improved summer thermal comfort with an optimal reduction of 1.0 PMV. These results provide optimized design decisions and updates on microclimate conditions to create a comfortable community park.

This experimental study presents the results obtained from open-return wind tunnel tests to investigate turbulent flows in a street canyon model. The inlet was conditioned using spires and roughness in order to simulate atmospheric boundary layer characteristics. Test were performed with a street canyon model, which consisted of two 50 mm tall blocks separated by a distance of 45.7 mm, at a fan speed of 46 Hz. Particle image velocimetry (PIV) was used to determine the local ensemble-averaged velocity vector fields at the mid-plane of the street canyon model. Proper orthogonal decomposition was used to identify the dominant modes of turbulent motions, which demonstrated a vortex core between the street canyon walls, as well strong sweep/ejection events. The PIV data provide detailed information about the fluid–structure interactions, which is the inertia force on particle trajectory, and can lead to entrainment and/or ejection of particles within the modelled street canyon.

Wind tunnel (WT) measurements for the validation of computational fluid dynamics (CFD) are essential to enable an accurate numerical assessment of complex indoor airflows in naturally-ventilated buildings. However, there is a lack of WT studies that employ realistic building geometries. The objective of this study is the validation of 3D steady Reynolds-averaged Navier–Stokes (RANS) simulations and scale-adaptive simulation (SAS) of cross-ventilation in a realistic building geometry using WT experiments. Therefore, velocities were measured in and around a cross-ventilated realistic building model with internal partitions by means of laser Doppler anemometry (LDA). The steady RANS simulations were conducted with the RLZ k-ε and SST k-ω turbulence models, whereas SAS was performed with the SST k-ω model. This study showed that the SAS significantly outperforms the steady RANS simulations.

Transpired Solar Collectors (TSC) are promising solar energy solutions and represent a sustainable alternative strategy to mitigate energy consumption for space heating, preheating of fresh air or drying purposes. During the last 30 years TSCs have been extensively investigated by mathematical and experimental study. However, one of the main problems remains the thermal storage when the heat source is off (cloudy weather, night period). Thus, it is necessary to couple a Thermal Energy Storage (TES) system with the TSC. In this study we are investigating the arrangement of encapsulation for TES, integrating phase change materials (PCM) in spherical elements. Spherical encapsulations were analysed via CFD (computational fluid dynamics) studies and validated via PIV (Particle Image Velocimetry) measurements. The model reproduces a part of a real scale thermal energy storage inserted in a Double Skin TSC. The model consists in a Plexiglas duct in which we studied two different arrangements for the spherical encapsulated PCM. For each of the arrangements we analysed the heat transfer between the TES elements and the air passing through the collector. The main conclusion was that hexagonal arrangement provides a better airflow passive control for the heat transfer enhancement than the rectangular arrangement. This study provides an understanding of the impact of the TES elements type on a solar facade efficiency, to be more comprehensive for engineers and architects in their designs.

The transcritical carbon dioxide (CO2) heat pump system is a promising technology for space heating and domestic hot water applications. The economic and environmental analysis of the transcritical CO2 ejector heat pump water heater system with a tri-partite gas cooler is researched in this study. A comparison with a traditional electric boiler is provided to illustrate the benefits of the proposed system. The results show that the initial capital cost of the heat pump accounts for 49.2 and 44.3% of the total life cycle cost in Norway and Switzerland, respectively. The sum of the installation and earthwork costs of the heat pump dominates the total initial capital costs. Compared with the electric boiler, the payback period of the proposed system is 10.7 years in Norway and 9.7 years in Switzerland. The heat pump can reduce the electricity and maintenance costs yearly by 64% compared with the electric boiler. The annual CO2 emission reduction of the proposed heat pump system is 73% in the studied locations.

Achieving the targets of the Paris Agreement as an international treaty on climate change requires global climate actions by all sectors, including ensuring that buildings are more energy efficient. Today’s modern buildings employ a worldwide well-known and versatile usable building material which is a new type of green low-carbon engineered wood product, cross-laminated timber (CLT), for their structural frames. CLT as an innovative plate-shaped product provides a laminated structure and great physicomechanical characteristics. This article studies the development status and implementation of CLT in Europe, emphasizing its material properties and load-bearing characteristics. The newest findings related to CLT are reported. Also, the environmental benefits of using CLT in the construction industry are discussed. Moreover, the energy performance and performance of the utilized CLT elements are highlighted. According to our findings, the embodied energy and embodied carbon are significantly lower in CLT constructions compared with reinforced concrete and steel structures. Finally, the prospects of CLT are presented.

A new type of heat-moisture treatment air conditioner terminal unit (ACTU) for heating, cooling and dehumidification is designed by using a parallel flow tube as the core element, attached to closed rectangular fins, and its performance and resistance characteristics are studied. The research results show that under indoor temperature conditions in winter and summer, when the water supply temperature is 60 and 7 °C, the circulating water flow is 0.5 m3/h, and the air volume is 150 m3/h, then the heat supply, cooling capacity and the dehumidification capacity can reach 758 W, 662 W and 721 g/h, respectively. The maximum resistance on the water and the air side is 4.66 kPa and 27.2 Pa, respectively. The thermal resistance of the ACTU is analyzed, and the performance optimization design is carried out. The results provide a theoretical basis for the application and optimization of the ACTU.

Mechanical ventilation equipped with heat recovery systems (MVHR) is prevalently used in the Scandinavian climate. These systems considerably reduce the ventilation heat losses during the long and cold winter seasons in such cold climate regions. The humidity in the return air from the building may condense due to exposure to low outdoor air temperatures. This affects the heat transfer rate between the two air streams and causes blockage if the condensate turns into ice. In this study, detailed heat transfer modeling of the heat exchanger enables anticipation of condensation and frosting onsets. Simulation results prove that the relative humidity in the return air and outdoor air temperature are the most decisive parameters for controlling the frosting threshold. Except preheating the outdoor air to above the frosting threshold, increasing the return airflow rate using a variable speed fan can be used for frost prevention or postponing the ice appearance. The results show that increasing the return-to-supply airflow rate ratio effectively lowers the frost threshold for the climate of central Sweden.

Thermal comfort in the buildings is provided by different type of heating, ventilation, and air conditioning systems (HVAC). Several efforts have been made for localized heating and cooling. Application of thermoelectric systems in space heating and cooling is among the recent solutions. Yet, the efforts were mainly remained in laboratory level. This work aims to optimize a thermoelectric system performance for localized heating application in order to achieve higher temperature difference with the environment. The application of the system was optimized through a new control scheme of the fan that is on/off operation instead of continuous functioning. This feature not only increased the temperature outlet of the device but also enhanced the coefficient of performance up to 11%. The new configuration facilitates the application of thermoelectric systems in building sector especially in localized space heating considering individual thermal comfort level.

This paper aims to quantify and enhance the energy flexibility of electrically-heated school buildings by adjusting the local temperature in the classrooms, while considering thermal comfort requirements. The study consists of four steps: (1) cluster analysis to identify different patterns in setpoint schedules for the classrooms, (2) development of data-driven grey-box models for the classroom clusters and calibration of these models with real data, (3) assessment of control strategies through rule-based approach with near-optimal setpoint profiles in the classroom groups, (4) quantification of the energy flexibility provided to the grid. A centroid-based approach (k-means clustering) is implemented. Then, system identification is carried out to identify appropriate reduced-order thermal network models for the thermal zones. The models are based on a low-order resistance–capacitance (RC) thermal networks along with state-space formulation. A building energy flexibility index (BEFI) is used to quantify the energy flexibility. Results show that by adjusting the setpoint temperature of classrooms, school buildings can provide energy flexibility of 35% during on-peak hours relative to a reference business-as-usual demand profile.

The application of radiant cooling systems is limited due to the condensation problem and insufficient cooling capacity, especially in hot and humid climates. One solution to the problem, Air-layer Integrated Ceiling Radiant Cooling Panel (AiCRCP), was proposed with an infrared-transparent (IRT) membrane separating the radiant cooling surface from the space air. Therefore, a lower radiant temperature is reached when the IRT membrane surface temperature is kept higher than the dew point temperature without condensation. Different from conventional radiant systems, the cooling capacity of AiCRCP is improved and redistributed. Besides, control strategies of hybrid systems (combine air cooling and radiant cooling) are not suitable to the AiCRCP. In this research, a dynamic simulation method of AiCRCP was developed by integrating the heat transfer model with the transient simulation platform Trnsys. From the results, the integrated model was validated from AiCRCP heat transfer model and error was less than ± 2.5%. Based on this method, the dynamic characteristics of the thermal environment and energy performance can be investigated by dynamic simulation in further study.

The design of a new retail store in Vancouver gave the opportunity to analyze different options for the building heat rejection. Three options were analyzed: Traditional heat rejection with roof mounted fluid coolers, ground rejection with a geoexchange network and rejection in district heating network at high temperature. Rejection in district heating system has many advantages as heat island reduction and use of rejected heat by neighbors for their domestic hot water heating needs thus reducing carbon emissions. High temperature heat pumps for heat rejection were used in conjunction with thermal tanks accumulators. The first step was to maximize all the energy recovery from internal sources to supply the building heating needs. In peak cooling periods, the heat pumps reject the cooling energy in the District energy network. The modulation of rejection is based on the thermal accumulation tanks temperature. The measured operation results show that there is almost twice the energy injected in summer in the district heating system compared to the energy extracted in winter.

Estimating transmittance value is a decisive factor to calculate the rate of energy scape in buildings. Various methods are available to determine this parameter in the literature and practice. However, they have some inconveniences (such as the fact that they are traditionally based on a limited number of measurements and expensive instrumentation). This paper introduces the application of low-cost sensors for the thermal monitoring of buildings. To do so, on the basis of the temperature-based method (TBM), an Arduino-based data acquisition device was developed to infer the transmittance parameter of building envelopes. This system is supported by an Internet of Things (IoT) platform which provides low-cost postprocessing and saving of data. The potential of this metering device has been shown by carrying out thermal monitoring of a box model in laboratory conditions. Finally, to ensure the monitoring accuracy, the obtained results have been compared with those obtained from ISO 10456/2007.

In the United States, approximately 65% of buildings were constructed before 1992, when the United States Department of Energy (DOE) established the Building Energy Codes Program (BECP). Building retrofit to meet and exceed present high efficiency building standards is a critical path to reduce the overall existing building energy consumption and associated carbon emissions. Despite the well-established advantages of building energy retrofit, its adoption has faced numerous challenges. Therefore, it is important to review how effective building energy retrofit approaches can be in specific climatic contexts. This paper aims to provide a systematic and critical review of various exterior retrofitting systems for energy conservation and efficiency in cold climatic conditions, to summarize findings and knowledge gaps, and to identify values and challenges in advancing building envelope retrofits. To this aim, a set of various exterior retrofitting representative techniques was collected from various projects around the world. A summary description of key design elements, thermal performance, airtightness, and improvement areas for each system is presented and discussed. The potential and limitations of each retrofitting approach are evaluated to aid further development of sustainable retrofitting systems. Concluding remarks also converge on the identification of remaining research questions to discuss various issues related to system performance, and to address future opportunities.

Double Skin Facades can greatly impact building energy needs and environmental comfort, due to the large modulation they could have on solar radiation transmitted, the heat and air flow across the façade. Within this paper a methodology and control architecture is presented, so that an embedded controller can be adopted to support such flexibility. This carries out tasks of measuring façade-level and boundary (indoor/outdoor) parameters, processing the data, running simulations, communicating with higher level automation and controlling the DSF actuators. This work demonstrates the potential of such an approach to measure and/or calculate local façade and building performance parameters (by adopting local information and locally simulated building envelope models), supporting higher level model-based decision making, by presenting the workflow and methodology, and the preliminary results from an experimental campaign.

This paper compares three experimental methods for measuring the specific heat capacity of building materials. Many new and emerging building materials do not have well measured and published specific heat capacities which hinders modelling accuracy. Several methods exist for measuring the specific heat capacity of materials, but many traditional methods are not appropriate for certain building materials. In this study the specific heat capacity of four common building materials was measured using a heat flow meter (HFM), a guarded hot plate (GHP) with heat flux sensors and a convective analysis tool (CAT). The HFM method produced results with the lowest experimental uncertainty, but required uniform, solid rectangular samples to conduct the testing and thus not applicable for use with non-rectangular samples. For materials that are either not a solid, or cannot be formed into rectangular, uniform samples, the CAT is the preferred method for determining the material’s heat capacity.

As nations develop CO2 reduction targets, the retrofit of heritage buildings creates an opportunity for achieving energy and carbon savings while mitigating Climate Change. This paper explores through three NZ theoretical case studies the benefits from energy retrofits of heritage buildings, including how improvements to the building envelope can reduce carbon emissions while protecting historic significance. Energy efficiency measures were assessed in terms of thermal comfort, life-cycle carbon and life-cycle cost. Results were used in interviews with experts to develop recommendations for assessment methods and criteria that could be incorporated into practice and policies. The analysis of different energy retrofits showed a decrease in heating energy, and a lowering the carbon footprint over 90 years when compared to the existing buildings. The paper discusses how balanced retrofits could be incorporated into policies targeting carbon reductions, and the benefits to be gained from promoting building reuse.

Open-plan offices have become a typical office type in current, due to economic reasons and facilitating information flow. Its defects in the acoustic environment received a growing concern since poor acoustic environments are harmful to occupant’s environment satisfaction, work productivity, and health status. In this study, a questionnaire survey was carried out in a construction company, and aims to examine the effects of acoustic factors (i.e. indoor noise level, speech privacy, and disturbance from 9 common noise sources) on acoustic satisfaction of architects and building structural engineers in open-plan offices and to explore whether there are differences in acoustic perception and demands between architects and building structural engineers. A total of 249 participants were asked to answer a series of questions. The results demonstrate the high correlations between acoustic factors (speech interference on respondent’s reconcentration and problem-solving speed, and perceived noise level) and acoustic satisfaction for both architects and building structural engineers, and emphasize that speech noises (nearby and distance colleague chatting, telephone conversation) are the main noise sources impacting acoustic satisfaction. Speech privacy is an important factor impacting acoustic satisfaction for architects, while it is not for building structural engineers. In addition, the analysis reveals that architect’s perception of acoustic factors depends on gender.

Traditional building envelope is static and cannot regulate its physical characteristics according to outdoor climate. A new ventilated concrete envelope proposed in this paper consists of insulation layer, airflow layer and thermal storage layer. In order to evaluate and optimize the thermal performance of this new envelope, the flow and heat transfer model was established based on the heat balance method and a demonstration house was tested to verify the heat transfer model. On this basis, the influences of insulation layer thickness, air layer thickness, air supply speed and heat storage layer thickness on thermal performance of the envelope were considered, and the sensitivity analysis was conducted. It was found that the air layer thickness and air supply speed had big influence on the performance of the envelope in summer, while the heat storage layer thickness and insulation layer thickness had big influence in winter.

In a traditional atrium design (i.e., a single high vertical space), natural ventilation (NV) is an attractive design but usually is limited by the fire department. This study proposes a novel NV design to mitigate this conflict, which includes a segmentation slab and ventilation shaft. The segmentation slab divides a high large atrium into upper and lower parts. The ventilation shaft can extract the smoke and maintain the performance of NV for daily use. To examine this ventilation design, a small-scale experiment and numerical simulation were conducted. The results show that this design can simplify the design of the mechanical smoke control system while maintaining the smoke control performance, i.e., thinner smoke layer depth. Furthermore, the ventilation shaft can perform a higher NV rate than the traditional ventilation design. This study can promote the NV application in the high-rise atrium.

Saturation water content and liquid diffusivity are two important parameters that influence hygrothermal simulation results, particularly when investigating the performance of wood-frame walls subjected to rainwater penetration. In this paper, the impact of saturation water content (free water saturation and effective saturation) of OSB in a wood-frame wall subjected to water penetration was assessed through hygrothermal simulations using two programs—WUFI and DELPHIN. The results derived from DELPHIN showed that the model using free water saturation and that using effective saturation had significant differences in the simulated moisture content of OSB during the water penetration period, whereas results obtained from WUFI did not indicate such differences. Thereafter, the accuracy of three liquid diffusivity equations and their influence on simulation results was assessed based on the free water saturation model by WUFI. The derived liquid diffusivity did not accurately reflect the measured liquid diffusivity of OSB, and the simulation results using derived liquid diffusivity were considerably different from those using measured liquid diffusivity.

This paper aims to study college students’ feelings about the indoor daylight environment of the library. Taking a university library in Beijing as an example, a combination of questionnaire and illumination measurement was used to investigate students’ choice of library seats and the time period when they were satisfied with the daylight environment in different seasons. In addition, the daylight simulation on illuminance was also used during the study. It was found that students have specific expectations of the daylight environment and the daylight environment influences their seating preferences and comfort in different seasons. Throughout the year, students preferred seats with adequate sunlight. However, the degree of satisfaction decreased with a too high level of illuminance. The range of the illuminance that most students are satisfied with is 900–1050 lx.

Off-site manufacturing of building components could be a solution to facilitate the construction and renovation of buildings in remote areas. However, the impact on local outcomes, transportation constraints, and the complexity of some systems remain challenging. In these communities, achieving optimal energy performance from central heating systems may require a complex pipe network. To support decision making during the design stage, this paper proposes a computational approach to optimize the grouping of components (i.e., modules of Prefabricated Plumbing Systems). The algorithm minimizes the system installation costs and CO2 emissions related to ship transportation, while maximizing the local job creation. Fuzzy logic models and NSGA-II algorithm are used to evaluate configurations and identify non-dominated solutions, respectively. For a case study with 40 components, the algorithm found solutions which reduce installation costs by up to 81.9%, generate up to 23.4 h of local jobs, but increase CO2 emissions by up to 36.7%.

Energy consumption in buildings has been studied for many years now, as the world’s environmental concerns arise. Consequently, the airtightness of the building envelopes is a hot topic for many construction firms. The present study investigates the use of numerical tools to predict the hygrothermal impact of air movements in a prefabricated wall assembly with a wall-to-wall junction. A numerical model was developed with the software COMSOL Multiphysics, where the wall is studied as a two-dimensional domain and the junction’s air opening is represented by a one-dimensional line. Experimental measurements were conducted in a climatic chamber to compare and validate the numerical results. Six different scenarios were tested in laboratory conditions and reproduced on the numerical model. Temperature and relative humidity at four distinct locations were measured at the surface and inside the wall. Numerical results provide somewhat reasonable agreement with the measurements. However, further calibrations must still be performed.

This paper explores the impact of innovative fenestration systems to counter the cooling energy use in buildings, exploiting the potential of passive technologies and contributing to better understand the role of dynamically operated building envelopes. The windows have the shading system integrated in the glazing unit and a sash with motorized opening, these functions can be operated by the smart building management system according to given control strategies. The study presents preliminary results of a wider project based on field monitoring and numerical analyses. The former showed that the combination of solar protection and night ventilation reduced the indoor temperature by 1.4 and 2.2 °C in the rooms equipped with new triple and double glazing unit, respectively, compared to those equipped with the existing old windows. A numerical model was calibrated versus monitored data, the thermal simulation in transient regime conditions demonstrated the solar protection and night free cooling might provide energy savings up to 76%.

Although many studies highlight the relevance of heat transfer through frames and aim at improving the thermal performance of the frame, unfortunately poorly insulated frames are still being sold in many regions of the world. Therefore, it is necessary to quantify the potential effect of strengthening on the window frame of nationwide. This study evaluates the impact of window frames on annual energy consumption and its contribution to CO2 emission reductions at the city-scale. The impact of frame material on annual energy consumption is approximately 0.11%–3.55%. Consequently, it was confirmed that the CO2 emissions can be reduced by approximately 4.76%–8.02% by alternative. In conclusion, when converted to the amount of CO2 gas absorbed by cedar, the absorption effect of 282,558–476,530 cedar trees can be obtained. Through this, it can be confirmed that, among the various elements of the building, changing the material of the window frame shows a significant energy saving effect, and that it is a considerable amount when converted into the city-scale.

The Tibet plateau presents low atmospheric pressure, low air density, high sky transparency. Hence the convective and radiant heat transfer in Tibet plateau are quite different from that in plains, which results the heat loss and insulation method of heat storage tank in plains are not applicable to that in Tibet plateau. Aiming at Tibet plateau, solar storage tank heat loss factors were analyzed. The heat loss per unit volume of storage tank with different volume, aspect ratio, inlet flow rate and insulation thickness were calculated by numerical simulation. And the thermal performance of solar storage tank was verified by experiment in Tibet plateau. The results show that the heat loss per unit volume in Tibet plateau increases by about 11% compared with that in plains. The recommended insulation thickness of storage tank is 60–90 mm in Tibet plateau. This study can be used as a reference for the thermal insulation design of solar storage tank in Tibet plateau.

This paper presents and discusses the development of a procedure required for the future application of machine learning (ML) in the review and investigation of infrared (IR) thermography images for building envelopes. A background on ML and existing applications within the building science industry is presented to capture an understanding of the possibilities available within this innovative method of evaluating deficiencies of building envelopes. Then, a breakdown of the groundwork and image processing required for the application of ML algorithms is reported. A procedure developed using a python coding environment is presented, demonstrating the process of taking an RGB IR image, grey scaling it and normalizing for the application of unsupervised ML algorithm. Similarly, for the purpose of supervised ML, the same approach is presented, followed by a procedure for the creation of segmentation masks using two types of approaches: temperature threshold and manually identified segments. A discussion of possible development of these new techniques concludes this paper.

The purpose of the study is to compare the heat transfer in three curtain walls equipped with different number of panes. The curtain walls are installed in a test building and are exposed to the Canadian climate. Temperature sensors are installed on the outside and inside surfaces of each section. Heat transfer profiles are presented for both a cold week and a hot week. Under cold weather, the curtain and spandrel walls with more glass panes loose less heat. Absorption of solar radiation by the frame and spandrel sections influence strongly the heat flux values.

In this paper, a novel passive dehumidification system was proposed, which combines passive dehumidification and solar collection (PDSC) system with energy recovery ventilation (ERV), to perform dehumidification and radiative cooling in summer and heat collection in winter. Fundamental theories of moisture movement during hot and humid summer based on thermodynamics chemical potential are explained. A detached experimental house was established and equipped with a PSE (PDSC combined with ERV) system. Energy recovery ventilation was installed in the attic to provide fanned indoor air circulation to the outside through the roof ventilation layer. The air circulation routes for dehumidification in summer are explained in detail. Five schemes were designed and conducted, with air-conditioning, ventilation method, and passive dehumidification system as parameters to evaluate the effectiveness and usefulness of the proposed system. The measured values indicate that the PSE system has superior dehumidification performance than other ventilation systems in summer.

Cool roofs, green roofs, and natural ventilation as representative passive cooling strategies are favored by many architects. However, existing studies have not fully explored the potential benefits of integrating these technologies, particularly when cool roofs or green roofs are used in combination with night ventilation. In this study, a field trial was carried out in Chongqing to assess the thermal performance of green roofs (GRs), cool roofs (CRs), aerated concrete insulated roofs (ACRs), and bare roofs (BRs) when combined with night ventilation for cooling. The results show that several functional roofs can reduce the external surface temperature of the roof structure layer by about 10 to 20 °C due to the shading effect. However, using those strategies separately has the potential to bring about poor thermal performance at night. A promising solution is combined roofing and night ventilation techniques, as the combination of these two techniques would be conducive to reduce indoor temperatures throughout the day. Nevertheless, it was concluded that a green roof is still a better roof solution than a cool roof or an insulated roof when combined with night ventilation.

This paper investigates the effectiveness of Phase Change Materials (PCMs) in building envelopes on the energy efficiency of Australian residential buildings. Using DesignBuilder simulation, a representative Australian single storey residential house was modelled with different PCM application strategies under a range of Australian climates. In this case study, PCMs could achieve 2.3–16.3% annual electricity savings depending on types of PCMs and climates except in Australian Climate Zone 1 (Darwin). Simulation results also indicated that applying PCMs on external walls would improve energy efficiency performance more than applying them to the ceiling, and PCMs on longer solar exposed walls performed better than those applied on shorter solar exposed facades. It was also found that the energy efficiency performance would decrease when the PCM melting point was outside the thermostat range of the particular climate.

Desiccant material is vital for dehumidification. Metal–organic frameworks (MOFs), with high water vapor uptake and mild regeneration condition, shows significant advantage in dehumidification. However, the practical application of MOFs in the built environment has been a problem since existing practical application forms of MOFs (e.g., powder, granule, coating, etc.) have low utilization of material and may lead to agglomeration of MOF particles. Herein, we developed a new MOF-based electrospinning nanofiber membrane and achieved a high loading rate of MOFs. MOF nanoparticles, evenly distributed on the fibers, have high utilization since they can fully contact moisture in air. Then, according to the maximum equilibrium moisture absorption tests results, MOF electrospinning nanofiber membranes have significantly higher moisture adsorption ability than other common textile and porous materials in the built environment. Finally, we established a building energy model to evaluate the effect of MOF electrospinning nanofiber membrane on building energy consumption. The simulation results show that the MOF electrospinning nanofiber membrane has an excellent energy-saving potential. The latent heat load removal rate can achieve up to 40–50% in relatively dry climates by a purely passive manner.

Balcony board embedded in the building exterior wall is a kind of thermal bridges, which increases building energy consumption. In this paper, the balcony structure was determined by referring to the relevant standards of residential buildings in hot summer and cold winter area in China. The two-dimensional steady-state heat transfer software, THERM, was used to simulate the balcony thermal bridge under different insulation schemes, and analyzed the effects of four insulation forms with the change of insulation layer thickness on heat flow distribution and the average heat transfer coefficient (K-value) of balcony thermal bridge. Results show that the thermal insulation effect of balcony bridge with external insulation is the best, followed by internal insulation, corner insulation and thermal break, thermal break. It is difficult to keep good heat preservation for balcony thermal bridge just by thermal break. The corner insulation and thermal break with insulation layer thickness of 60 mm can save the cost of insulation, and achieve similar insulation effect to the external insulation.

The reliability of the analysis of heat, air, and moisture transfer phenomena in building envelopes depends on hygrothermal properties inputs of construction materials in the exterior layers of the building envelope, including cladding materials. Due to the dependency on temperature and relative humidity, determining hygrothermal properties for a building material is technically challenging and needs substantial time and resources. In this paper, thermal conductivity, adsorption isotherm, vapor permeability, and water absorption coefficient of stucco, clay brick, fiber cement board and western red cedar were measured at nine different climate conditions as a combination of the different temperature ranges of 3–60 °C and humidity range of 50–90%. Our results indicated that the water vapor permeability and water absorption rate in all cladding materials increased with increasing temperature, whereas the adsorption isotherm decreased with temperature. However, the thermal conductivity of all tested cladding samples increased linearly upon rising temperature.

The flammability and serious melt dripping illustrate polymer’s fire risk. The thermal shielding effect of carbon nanotubes has been widely concerned. There were a series of experiments about ignition, heat release, horizontal flame spread and pool fire, which were carried out to analyse the fire risk of polymer composites including polymethyl methacrylate and multi-walled carbon nanotubes (MWCNTs). The results showed that after the addition of MWCNTs, ignition time increased, peak heat release rate decreased, and flame spread rate rose, indicating ignition risk decreased, heat release risk dropped, and fire growth risk increased, respectively. Although MWCNTs retarded the dripping of molten polymer, there was an increase in pool fire height when the content of MWCNTs exceeded a certain amount, indicating the increase of the risk of pool fire. Therefore, the fire risk of polymer composites cannot be measured in a single way, and a comprehensive assessment of multiple factors is needed.

To meet UK government targets of 22% reduction in greenhouse gas emissions (1990–2035), large scale retrofit of the existing housing stock is necessary. This paper empirically investigates the performance of a low-rise block of flats pre- and post-retrofit. Pre-retrofit findings informed retrofit design, and provided a benchmark for post-retrofit performance. Pre-retrofit, airtightness was better than expected (3.2 ach @50 Pa), informing the sizing of mechanical ventilation units. Post-retrofit airtightness (0.67 ach @50 Pa) was lower than design target. Weather-corrected heating season gas consumption was 60–62% lower post-retrofit (Jan–Apr 2021) compared to pre-retrofit (Oct–Dec 2019). High pre-retrofit relative humidity (RH) (> 60%) led to prolific damp/mould in several flats. Much lower post-retrofit RH eliminated damp/mould, but was often below recommended 40–60% range, with potential negative health impacts. Indoor temperatures were lower and less extreme post retrofit. Overall there was little evidence of rebound effect post-retrofit.

Thermal mass plays an important part in the transient thermal performance of wall assemblies and buildings. It has been shown that if the thermal mass is used correctly, it can improve the thermal performance of wall assemblies and hence the energy efficiency of buildings. The position where a thermally massive layer is placed within a wall can affect the transient thermal behaviour of the wall assembly. In this study, we simplified a wall assembly into simply two thermally resistant components: that of insulation and a thermally massive layer. The transient thermal behaviour of the wall was then investigated concerning the different placement within the wall of the thermally massive and insulation layers. Two cases were studied: (i) a three-layer wall having one layer of insulation and two thermally massive layers, and (ii) a three-layer wall, one layer of which is thermally massive and the other two layers of insulation. For each of the two case studies, walls were shown to have identical values for overall thermal resistance and heat storage capacitance. The interior temperature was assumed to remain at 21 °C, and three climate conditions were considered for exterior, representative, respectively, of heating-dominated, temperate, and cooling-dominated weather conditions. The results showed that amongst the case studies for the set (i), the wall that had the optimum transient thermal performance was that for which the insulation layer was placed at the exterior and the thermally massive layers were placed at the interior side of the wall. Whereas the case for which one thermally massive layer was sandwiched between two insulation layers showed the minimum energy demand for the case study set (ii). Based on the cases considered and the corresponding results, it can be concluded that to improve the transient thermal performance of a wall with a given amount of heat storage capacitance and heat resistance, the thermally massive layer(s) should not be directly exposed to the fluctuations of the outdoor climate.

Air infiltration is responsible for a considerable amount of the energy losses in buildings. While energy codes require testing building envelopes and set minimum thresholds for infiltration, leakage goes mainly unstudied in countries with no or weak building energy codes, such as Egypt. This research reports some of the first air infiltration findings in Egypt’s residential sector. Two residential spaces are tested using a blower door test (Minneapolis Blower Door™ System) following ISO 9972:2015. The testing is complemented by a qualitative examination (thermal imaging) to identify critical infiltration routes. Despite the use of heavy building materials, such as concrete, and bricks walls, the findings indicate significant leakage in residential spaces exceeding the 10 m3/h m2 at 50 Pa suggested in regional codes and most thresholds set in European regulations. Windows and their frame-wall interfaces and air conditioning conduits that penetrate the walls are found to be critical leakage paths. We estimate that leakage results in significant energy losses, especially during the cooling season, causing up to 1.8 kW of sensible heat gains during the summer. The findings constitute an urgent call for further studies on the issue and for developing economic remedies for infiltration in existing and new structures that fit the local construction techniques.

The construction industry has a large environmental impact considering the building material manufacturing, construction, operations, and decommissioning. Not surprisingly, there is an increasing request to reduce this significant environmental impact through the use of materials with low global warming potential (GWP), which could also guarantee efficient building operation. Insulating materials reduce the building energy demand, but they have a negative environmental impact for the raw material extraction, transportation, manufacturing, and end-of-life disposal. This article focuses on closed cell foam materials and present recent advancements in the fourth generation of blowing agents. The article also discusses the importance of considering the effective thermal conductivity to account for the in-field long-term performance of these insulating materials. The aim of this paper is to promote a more holistic thinking about the characteristics of foam insulating materials.

The impact of heat flow through conductive vacuum insulation panel (VIP) enclosure material on the overall panel R-value has been evaluated by finite element modeling over a wide range of panel dimensions and material properties. The determination of “edge heat loss” is necessary since the R-value testing of VIPs is limited to a central area (RCOP) while the effective thermal resistance, REFF, includes the total heat flow. Project results are the ratio REFF/RCOP as a function of kE · AE/kC · AC or θ where k is apparent thermal conductivity and A is cross-section area perpendicular to heat flow direction (subscript E for enclosure and subscript C for core). Results are represented to within 0.1% by REFF/RCOP = 1 − θ · (0.9798 − 0.8221θ + 0.3721θ2).

Residential buildings in Jordan make up 72% of the total share of buildings. However, it is not yet a common practice to investigate Operational Carbon (OC) and Embodied Carbon (EC) of buildings following a holistic approach. This study aims to investigate the intricate relation between OC and EC to assess the feasibility of achieving net-zero carbon performance in dwellings in Jordan. First, the most common archetypes of residential buildings are identified. Next, thermal simulation is used to evaluate how these archetype dwellings might perform in terms of carbon emissions based on existing material and building services used in the construction supply chains. Finally, pathway scenarios to improve carbon footprint is investigated to find a set of technically feasible solutions for new buildings using a baseline defined for 2000s apartments archetype. The results show that implementing three levels of carbon emissions reduction measures can save up to 64% of annual OC and around 30% of EC compared against the baseline scenario.

Electrification of space heating can help reduce greenhouse gas emissions, especially when coupled with advanced control strategies and leveraging building energy flexibility. A methodology to automatically generate accurate control-oriented building models is an important part of quantifying the energy flexibility of a house. This study presents an automated procedure for multi-zone RC model development using measured data from smart thermostats for the quantification of potential energy flexibility in residential buildings. The model is intended to forecast day-ahead indoor air temperatures using predictions of solar radiation and ambient temperature. The model generation procedure starts with a very simple model with one thermal capacitance in each controlled zone and iteratively adds/removes one RC parameter at a time—the one that increases the accuracy and robustness of the model the most. The methodology is applied to measured data from a house in Quebec. The calibrated model is then used to quantify the heating load flexibility of the house during a demand-response event and optimally control the heating output in a Model Predictive Control framework, reducing its power peak demand during the event by 63%.

Aerogel is characterised by high thermal insulation and light transmission, suitable for building envelope applications. Several aerogel-based products are available on the market, but innovation is still possible at both material and components levels. Aerogel glazing systems over perform standard one in winter, however the high insulation and high transmittance may cause overheating and glare in summer. This paper deals with optical and thermal characterization of an innovative glazing unit with a mix of aerogel granules and powder in the gap. The inclusion of powder could reduce light and solar transmittance, without compromising thermal resistance. Glazing systems with and without powder were assembled in different thickness and powder percentage. Optical properties were measured using a built-in spectrophotometer and the thermal resistance of selected samples with a hot-plate facility with the guard ring. Results demonstrated the impact of the aerogel powder in reducing solar gains in order to extend the current application of aerogel panels also to hot climates without neglecting the daylighting performance of the built environment.

A Finnish-German collaborative project was carried out in 2018–2020 with the objective to investigate the accuracy and reliability of a novel metering device for in-situ determination of U-values of envelope structures. A rapid and reliable in-situ measurement method would clearly be a desirable tool for surveying the actual energy performance of existing buildings. Series of experimental and computational studies around a novel measurement device were carried out in Rapid U project. Rapidness of the measurement is sought by scheduling the moment of measurement so that the outdoor temperature has been sufficiently stable and cold before the measurement. Several test building measurements were carried out for certain structures in real weather conditions. Numerical modelling was also used to estimate the total number of hours in a year when the conditions are theoretically possible for similar structures to be measured rapidly. Repeated test building measurements showed unfortunately significant variation in the results. However, on average the results were promising and according to the simulations there should be plenty of suitable hours per year for rapid U-value measurement in cold countries if certain error tolerance is allowed.

This study investigates the performance of a thick supply-air window with PCM cavity-frame (PCMCF), based on experiments conducted in a guarded hot box. In the experiments, the thick supply-air windows with and without PCMCF were compared, by increasing the air temperature of cold box from 10 to 25 °C and then decreasing from 25 to 10 °C. The experimental results showed that using the PCMCF could reduce the increasing and decreasing rates of outlet air temperatures. In addition, due to the use of PCMCF, the time lag was 53 min in the heating process and it was 21 min in the cooling process. This reveals the application potential of the PCMCF to reduce overheating phenomena and to improve energy performance of supply-air windows.

The present research investigates the active–passive system for cooling application. The Phase Change Material (PCM) plates are applied to the internal wall and ceiling of the PCM modified test cell forming an airgap with its original wall and ceiling which is nocturnally ventilated to decrease the PCM solidification time. The Active–Passive System (APS) system was investigated under different daily melting cycle cases (indoor air temperature of 26, 30 and 35 °C) to test the cooling effect of the system and different nighttime solidification cycle cases (inlet air temperature of 15, 16 and 17 °C) and determine the required solidification time. The results show, that during the hottest daily case the indoor air temperatures dropped to up to 5 °C. During the night cases with inlet temperature of 15 and 16 °C and flowrate of 400 m3/h, the PCM plates solidified in 5 h and 14 h, respectively.

For carbon neutrality, it is important to continuously utilize wood with a high carbon stock. CLT using low-grade wood is an eco-friendly building material that can accelerate the realization of high-rise wooden buildings and carbon neutrality. In this study, basic studies were conducted to apply PCM, which stores thermal energy in the form of latent heat, to CLT to improve the low thermal storage performance of wood materials. To prevent leakage of liquid PCM, Biochar, a porous material, can be produced by pyrolysis of various biomass, and in this study, biochar obtained by low-temperature pyrolysis was used. The thermal properties of PCM/biochar composites were analyzed through DSC, TGA, FT-IR, and SEM.

As the construction industry is shifting towards a lean manufacturing model of off-site construction, non-value-added activities are to be eliminated. Panelized building envelopes with fewer components are desirable to reduce construction materials and cost. Due to builders’ experience with Expanded Polystyrene (EPS), it is prone to fractures and punctures during fabrication and transport of prefabricated wall panels. An alternative panel material with better durability is desired. Expanded Polypropylene (EPP) is commonly used in the automotive industry for impact resistance. EPP is a promising material, yet to be utilized in the construction industry, that could be widely adopted to create durable and resource-efficient building envelope systems. EPP is investigated in this study to understand whether it has a positive contribution to the structural integrity of a wall panel and whether a minimal addition of structural reinforcement is feasible. Finite Element Analysis was used to determine the load capacity of EPP, as well as with Oriented Strand Board and steel framing as structural components. This research will be of interest to builders and architects looking to expand the application and exploit the advantages of panelized construction.

For an efficient moisture performance analysis of walls, the selection of wall orientation for undertaking the hygrothermal simulations plays an important role. The wall orientation receiving the highest amount of annual wind-driven rain (WDR) and the one receiving the least annual solar radiation is that which is recommended in ASHRAE 160. There are different methods to select this orientation, called “default orientation” and the objective of this study is to answer two questions: (i) Are the different methods consistent with each other in terms of default orientation selection? (ii) Is the default orientation, as suggested by any method, identical to the orientation that actually (using simulation outputs) leads to the worst moisture response? Four methods for selecting the default orientation were investigated: ASHRAE, ISO, Climatic Index (CI), and product of rain and wind speed (R*v). Simulations were carried out under historical climate of different Canadian cities for two different wood-frame 2 × 6 inch wall systems that differ by their claddings: brick veneer and stucco. A moisture source of 1% of WDR was also assumed to be present on the exterior side of the sheathing membrane. Four cardinal orientations (North, East, South, and West) and the default orientation, as suggested by a given method, were evaluated in this study. Results showed that the ASHRAE and ISO suggest the same default orientation for most of the cases and this orientation also results in the worst performance on the basis of actual simulation results.

Aerogel-based coating mortars have declared thermal conductivities of 0.03–0.05 W/(m · K), similar to those of conventional thermal insulation materials. Due to the high porosity and fragility of aerogel granules, the material obtains a reduced mechanical strength compared to conventional mortars. Recently, there has been a large research effort on developing new mixtures with improved thermal and mechanical properties. This paper presents and evaluates two-dimensional numerical simulations, based on the random packing technique, as an alternative method to laboratory measurements in predicting the effective thermal conductivity of these mortars. Experimental data from the literature, on thermal conductivity of aerogel-based coating mortars containing 50–90 vol% aerogels were used to validate the simulation results. In this preliminary validation study, a relative error of 6–10% was observed. Future work can focus on improving the accuracy and including the prediction of mechanical properties in the suggested model.

This paper introduces new and handy mathematical expressions for predicting and understanding the effective thermal conductivity of two-phase homogeneous mixtures. In a two-phase mixture, particles of the dispersed phase are mixed in the matrix of the second phase. A primary purpose of the proposed model is to predict and understand the effective thermal conductivity of high porous thermal insulation materials, for which many other similar analytical models offer less accuracy. The presented expressions assume a perfectly packed mixture and consider the thermal conductivity of the two phases, the proportion and the particle size of the dispersed phase. The proposed model includes non-dimensional heat loss factors, calculated by two and three-dimensional numerical simulations. In the continuation of the work presented, an experimental study is planned to evaluate the accuracy of the model, compared to other existing analytical solutions, and the validity of the assumptions made.

The objective of this research is to investigate the effects of different bias correction (BC) methods applied to climate data used to assess the hygrothermal performance on building envelopes based on simulations. To this end, a univariate and two multivariate distribution based bias correction methods were used to prepare weather files to assess the hygrothermal response of a wood frame wall assembly at the Ottawa International Airport. The effectiveness of the bias correction methods was evaluated by comparing the bias corrected climate data to observational data, as well as by comparing their hygrothermal responses as obtained through simulations. The bias correction improved upon the raw Regional Climate Model (RCM), which significantly overestimated the average annual rainfall, relative humidity (RH), and temperature, whilst underestimating the wind-driven rain (WDR). Despite improvements made to the climate data through bias correction, performance indicators such as the moisture content (MC), mould index (MI), and RH on the exterior of the OSB had a diverse response to the climatic conditions. Generally, multivariate BC methods are seen to perform marginally better than the univariate method in this context. However, BC was not able to produce the same time series of climate variables, which have a significant impact on the wetting and drying conditions, and consequently the risk to mould growth in wood frame wall assemblies.

The intention of this study concerns the analysis of thermo-optical changes in a glass block system coupled with photovoltaic (PV) and phase change material (PCM). As solar simulators are considered key methodologies for conducting solar-based research under a well-controlled environment, this study employs small-scale solar simulator tests to reveal spectral and dynamic changes in a PV and PCM glass system. Therefore, the experimental setup was equipped with a spectrometer, and the spectral light transmittance data were obtained. This was also implemented for the dynamic transmittance analysis to characterize optical-responsive mechanisms in developing a semi-transparent system with PV and PCM. Though results revealed that spectral transmittance data provide a new insight subjected to PV and PCM characterization, certain limitations were drawn that the selection of light source is crucial, and the addition of a PV layer causes some specific problems to account for the edge transmittance effect.

Numerous studies have been conducted on the retrofitting of brick masonry walls with thermal insulation. However, these studies did not explore the concurrent aspects of increasing internal insulation, wind-driven rain levels, brick durability, and varying brick properties. This work therefore investigates the impact of adding thermal insulation on frost damage by using the hygrothermal simulation program WUFI 6.5 Pro. The properties of five different types of bricks were used in the hygrothermal simulations, and the proposed walls were subjected to two levels of wind-driven rain exposure. The results showed that the effect of adding thermal insulation on frost damage risk is influenced by several factors, the most significant of which are the bricks’ properties and the moisture exposure level. Adding interior insulation lowers the temperature in brick, increases moisture content significantly in that interior layer, and increases freeze–thaw cycles and frost damage cycles mainly in the second and middle layers.

The design of windows significantly affects the energy consumption in buildings. To figure out the impact of various window parameters such as window form, frame material, glazing layers, and Low-E coating on the energy performance, 3407 products were collected from the windows acquired China fenestration energy efficiency labels, which were divided into 18 window types according to different window parameters. Four models from Canada, the U.S., Europe, and Denmark were used to calculate the energy rating of the surveyed windows. Results show that non-TBA windows with low U-value can reach better performances in the heating season, while windows with Low-E coating which have low SHGC can reach better performances in the cooling season. Accordingly, different factors of window types need to be comprehensively considered to improve the energy performance of buildings in future designs.

There is no clear indication of critical moisture conditions for cross-laminated timber (CLT) envelopes exist regarding moisture content. Therefore, our main objective in this study was to set hygrothermal criteria for the design of CLT external walls in terms of moisture conditions. The focus is on five different types of CLT external walls that differ in their dry-out. The key factors in the safe hygrothermal design of the CLT external wall are sufficient dry-out capacity and control of the CLT moisture content during the construction phase.

Policies, programs, and building code updates are being developed to reduce greenhouse gas emissions from existing buildings. To assess their impact, building energy models representing existing Canadian commercial and institutional buildings have been developed and implemented in the Building Technology Assessment Platform (BTAP). The simulated annual energy intensity for existing schools and offices are compared to Ontario public building data for 2011–2017 for several Ontario cities to evaluate how well the models predict actual building energy use. The annual energy use intensity (EUI) of existing schools modeled without ventilation systems (0.51–0.65 GJ/m2/year) compares well against mean Ontario school EUI data (0.67–0.79 GJ/m2/year). The modeled annual EUI of existing offices (0.48–0.79 GJ/m2/year) is lower than the mean of administrative buildings (offices) reported in the Ontario data (1.0–1.3 GJ/m2/year). These existing building models can be used to evaluate the approximate relative impact of policies, codes and efficiency measures on existing building performance.

The Swedish environmental assessment certification system, Miljöbyggnad, rewards lower energy use in comparison to that of building regulations, related to total heated floor area. Previous studies suggest that this indicator shows the efficiency of the technical design of the building; not how well the building is utilized. Thus, a more suitable quantity/unit can be the one that takes into account the extent of how the building is used in a more efficient and sustainable way. The aim of this paper is to discuss if current energy units are sustainable enough to assess efficient use of buildings and investigate how indicators can affect the grades and criteria of a certification system. Normalization of simulated energy use of a school building, by using the total heated floor area, the total building’s enclosing area, number of occupants and the schedule of occupancy, give different results.

Building codes offer a promising opportunity to help ensure high-performance buildings through efficient building design and construction practices. Given that human behaviour is increasingly recognized as essential in building energy use, the validity of occupant-related assumptions in the building code and the energy performance modelling tools used to demonstrate code compliance are central concerns. This research’s main objective is to quantify the energy impact of occupant-related assumptions in the National Building Code (NBC) of Canada compared to new and emerging data sources. Four occupant-related assumptions are investigated with 11 residential building archetype models in different Canadian climates to assess their impact on annual energy use. Our analysis indicates that using the NBC occupant-related assumptions yields higher energy demand than the new and emerging data sources analyzed. Therefore, the findings presented in this study illustrate a need for revising the current occupant-related assumptions in building codes.

There is a debate about whether to adopt the intensity-based or reference building approach for the performance path of the Canada’s National Building Code (NBC). This paper presents the analysis of a statistically representative sample size of 240 Canadian housing archetypes to compare potential design outcomes of the two approaches. The 240 archetypes were classified into whether they performed better under the intensity-based or reference building approach. The simulation results showed that the archetypes favored by the reference building approach used 33% more energy and had a larger wall to floor area ratio by 91% than those favored by the intensity-based approach. These results indicate that the adoption of the intensity-based approach for the performance path of the NBC will encourage builders to design more energy-efficient houses with lower energy use compared to the reference building approach.

Building energy codes are recognized worldwide as a proven policy for achieving economy-wide savings in buildings. While codes used across nations tend to vary in format and approach, several countries share the same key issues. These often include challenges such as the need for faster and easier methods to verify codes in practice, requirements for existing buildings, and the ability of codes to adapt to new and advanced technologies. The paper findings draw from interviews and surveys of different building energy codes practices found in member countries of the International Energy Agency’s Energy in Buildings and Communities Programme (EBC) Building Energy Codes Working Group (BECWG), with a deeper look at practices related to codes compliance. The paper identifies and draws conclusions on emerging practices involving building energy codes across member countries.

This paper presents a study investigating the user mental models (UMMs) of ten Canadians, related to home heating systems and smart thermostat (ST) usage. The work contrasts previous investigation (Revell and Stanton in Appl Ergon 45:363–378, 2014), shifting the focus towards STs. Interviews evaluated self-reported behaviour and generated diagrams of UMMs. The research was conducted remotely, using video-calls and an online whiteboard tool. Overall, participants’ conceptualizations echoed a UMM previously observed in literature, recognized as feedback theory. This paper presents novel investigation of ST users, employing comparable methods with previous work. It contrasts results with literature and demonstrates a more uniform ideology among ST users. Furthermore, it utilizes novel methods that support the collection of qualitative data, that can be applied to future studies.

In order to reduce the repeated work of designers, this study constructs an implementation method of collaborative simulation of lighting and interior design on the same interface. Aiming at the requirements of information transmission and high-precision lighting simulation software in the collaborative design process, this study compares two lighting software, ElumTools and DIALux evo, which use Revit API and IFC file respectively for information transmission. Through CIE 171:2006 test cases, it is found that DIALux evo has more advantages in lighting calculation performance. At last, an implementation method of collaborative simulation based on Revit, including scheme design, calculation, and results return and evaluation, is finally proposed. That is, using IFC file and Revit API together for information transmission and DIALux evo as lighting calculation software can be proper.

In this paper, a methodology suggested by IEA EBC Annex 80 was used to generate future weather files and detect heatwaves in future and historical periods for Copenhagen. The result shows that the method got a relatively conservative prediction of temperatures in the long-term future. The duration of heatwave results detected by the new definitions are similar to the results of Danish Meteorological Institute (DMI) warmth heatwave definition. For the future period, two kinds of heat wave results were generated which based on historical and future period temperature data respectively. The results suggestion that the change of the adaptability of people and buildings to heat stress with the rise of the overall temperature should be considered when detecting heat wave events (HWEs). Furthermore, taking average temperature into account contributes greatly to obtaining the complete and independent HWEs.

The accuracy of building energy modelling (BEM) is challenged by the highly variable occupancy schedules, especially for residential buildings. In current practice, these models assume the same standard profile to represent occupancy in residential buildings regardless of the weekday, season, and household characteristics. To overcome these limitations, a larger dataset is required to develop dynamic, scalable occupancy models that can empower BEM tools and improve their reliability of modelling energy demand. To this end, this paper presents a methodology to use smart thermostat datasets to develop stochastic annual occupancy schedules for residential buildings. The proposed model is based on a duration probabilistic model integrated with the Markov-chain model to generate occupancy schedules that reflect the impact of time of day, day of the week, and seasons on the occupancy of residential buildings. The model showed an average accuracy of 71% and that the number of occupants in a given house has the most significant impact on the generated occupancy schedules.

It is reported that people spend 80–90% of their time indoors and previous studies show the vast majority are typically unsatisfied with their thermal comfort. To this end, the goal of this study is to develop a framework to improve thermal comfort in multi-occupant spaces in commercial and institutional buildings. Office occupancy is more variable during the covid-19 pandemic and is likely to remain so. The primary objective of this study is to leverage novel technologies such as wearables to identify thermal comfort levels in multi-occupant spaces. The secondary objective is to minimize thermal discomfort in these spaces via a consensus algorithm to keep most office occupants comfortable while decreasing energy consumption and to predict the optimal setpoint via classification machine learning algorithms such as k-means clustering from sensor data such as room temperature. The developed framework will be implemented in the recently established living lab at Concordia university which features co-working spaces.

Taking a campus dormitory building as the research object, TRNSYS was used to simulate the three heating systems of the building with air source heat pump (ASHP), electric boiler (EB) and ASHP coupled with EB, respectively. The parameters of indoor temperature, system energy consumption and energy efficiency ratio COP are used as evaluation indexes to compare and analyze the performance of the three modes. The results show that: the total energy consumption of the ASHP coupled with EB heating system during the heating period is 433,978 kWh. Among which, the electrical energy consumed by the ASHP is 329,567 kWh, and the average COP of the heat pump is 2.64, which is 0.17 higher than the average COP when the ASHP system alone is used for heating. Moreover, the best economy is achieved when the ASHP accounts for 80% of the coupled heating system.

This study explores the spatial variability of the thermal responses of older occupants’ in indoor environments at various locations in Montreal, Canada. Urban climate in and around the city over an extreme heat event in 2018 is simulated at 1 km spatial resolution using Weather Research and Forecasting (WRF) model, and data for 29 long-term care building locations in the city is extracted. A long-term care building was modelled using EnergyPlus and calibrated with measured hourly indoor air temperatures; it was then used to evaluate how local weather conditions affect indoor thermal conditions. The building simulation results were then applied to a physiological model for older (65y+) people that permitted evaluating individual's thermal responses. Further analysis was conducted by comparing the difference between responses of older and young occupants and between people’s responses in original and retrofitted buildings. More than 4 ℃ variations in the standard effective temperature (SET) of older occupants are observed in the studied buildings. The conclusions highlight the importance of applying strict indoor temperature limiting thresholds or retrofit requirements for long-term care buildings.

This paper proposes a novel frequency domain methodology for identification of the thermal response of representative houses in Québec. The methodology, developed in a MATLAB environment, is based on a data-driven frequency domain approach. Since buildings have a strongly periodic thermal behaviour during different seasons, frequency domain analysis is useful to determine specific trends, focusing on both long- and short-term behaviour. The analysis is carried out for different types of Quebec houses: bungalow, split and cottage—about 30 houses monitored by Hydro-Québec. The results highlight the common trends between different house archetypes and evaluates the frequency response G(n) of each thermal zone of a house, analysing the influence of solar radiation, outdoor temperature, and internal heating sources on the indoor environment. This study introduces a technique to assist in the development of reduced order linear models by neglecting or aggregating certain adjacent thermal zones of houses by examining the frequency response of the indoor temperature as a function of the outdoor temperature. Moreover, the zones with the most potential energy flexibility can be easily identified with an index that focuses on the frequency response of the indoor zone temperature as a function of the heating input. This identifies where to apply predictive control strategies to activate the potential energy flexibility.

This paper presents a methodology to analyze data from smart thermostats aiming at proposing a black-box prediction model of energy consumption for three common house archetypes (bungalow, split, and two-storey building) in Québec. Through a multi-step data clustering approach, based on a combination of Gaussian mixture model (GMM) and partition-based method (k-Means), hidden patterns and outlier values in the dataset can be identified. The purpose of this paper is to create a general methodology able to assess the reference consumption profiles of housing joining a grid-interactive program with dynamic pricing of electricity. The results of the proposed methodology identify demand-response (DR) events during the considered winter period and reference energy profile evaluating different combinations of external temperature and global horizontal irradiation. The final data calibration, performed in MATLAB, evaluates the macro-parameters related to occupant behaviour and passive solar gain for each house archetype. The proposed model has been compared to the business as usual (BAU) procedure in terms of accuracy in prediction, showing CV-RMSE of 9–16% compared to 21–40% of reference.

Buildings consume about 40% of the electricity generated and significantly burden the energy sector. A new concept of net-zero energy building has emerged to ease this burden. The preliminary design relies heavily on the simulation data from building simulation engines like Energy Plus. Research has pointed out about a 30% difference in average between actual and simulated energy consumption. One of the primary reasons for this discrepancy is pointed towards the use of schedule-based occupant behavior (OB) during simulation. This conventional schedule should be replaced by a model that could predict the occupants’ actions reasonably. Binomial Logistic Regression and Neural Network architectures were developed and tested for their prediction accuracy of window's state in the residential dorms of a local university at Syracuse, NY. The neural network models outperformed the Binomial logistic regression model by a considerable margin when both of them were tested in entirely different homes. We found that the Deep Neural Network model with ‘Adam’ optimizer and ridge regularization parameter of 0.01 performed the best to predict the state of the windows when the learning rate was 0.01. These models were then integrated into the Energy Plus (v 9.6) using newly released E+ Python API and tested in a single zone of the campus building. Using the ANN-driven schedules, we observed that the total sensible monthly heating load requirement for the month of September is reduced by 38.56%, and the total sensible cooling load requirement is reduced by 55.52%.

New York State (NYS) has over 750,000 dwelling units with inefficient HVAC and domestic hot water (DHW) supply. To explore the opportunities of carbon neutrality, in this paper, we investigated and evaluated an integrated HVAC and domestic hot water system that could meet all the heating, cooling, ventilation, dehumidification, DHW and air cleaning requirements for a single dwelling unit. To evaluate its feasibility in the U.S., lab testing was conducted at first. It focused on the air side to evaluate the system capacities in an environmental chamber that could mimic different outdoor conditions in lab space. Then, a nation-wide EnergyPlus simulation was conducted in seven different climate zones. Lastly, a nation-wide energy simulation using MPC was also conducted. Compared to the results of EnergyPlus, it showed around 60% energy saving for both cooling and heating seasons.

The objective of this study is to assess the effectiveness of wearable cooling in improving thermal comfort for a warm environment that would become prevalent due to more frequent extreme weather events, especially when air conditioning is not accessible for many developing countries. The experiment was conducted in an environment room with air temperature maintained at 31 °C and relative humidity at 55%. The study tested 30 participants using a wearable cooling device at the upper back location, while another 30 had no local cooling as the control group. Participants’ thermal comfort, thermal sensation and other metrics were assessed three times for a test session. The clothing insulation was 0.36 clo to simulate summer attire. The results showed significantly lower average local and whole-body thermal sensation for the participants with the wearable cooling device than the control group by considering all the votes during the entire session. Compared to the baseline, in particular, the local cooling group indicated a significant reduction in local thermal sensation for all three times of self-evaluation. Nevertheless, the reduction in overall thermal sensation occurred right after the local cooling was applied. Such a significant reduction was not observed after a while and then emerged again during the test, indicating an interactive phenomenon involving thermal adaptation and comfort restoration which will be investigated in the future.

Water heating is the third largest electricity consumer in U.S. households, after space heating and cooling. Thus, water heaters represent a significant potential for reducing electricity consumption and associated CO2 emissions of residential buildings. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity consumption and the CO2 emissions of a heat pump water heater without affecting user comfort. In this approach, a set of RL agents focusing on either electricity saving or emission reduction, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on different hot water usage and Marginal Operating Emissions Rate (MOER) profiles. The testing results showed that the RL agents that focus on electricity saving can save electricity in the range of 12–22% by operating the water heater with maximum heat pump efficiency and minimum electric element utilization. On the other hand, the RL agents that focus on emission reduction reduced emissions in the range of 18–37% by making use of the variable MOER values. These RL agents used the heat pump and/or an element when the MOER values are low due to the availability of renewable energy sources (e.g., solar and wind) and mostly avoided the periods of carbon-intensive periods. Overall, these results showed that the proposed RL approach can help minimize the electricity consumption and the CO2 emissions of a heat pump water heater without having any prior knowledge about the device.

This paper contributes to the occupancy prediction problem by developing state-of-the-art deep learning models. The occupancy prediction problem is addressed from two different viewpoints: multi-label classification and a sequence-to-sequence time-series analysis using encoder-decoder architectures. The following deep learning algorithms are employed in this study to construct occupancy models: multi-layer perceptron (MLP), recurrent neural networks, long-short term memory (LSTM), gated recurrent units (GRU), and bidirectional LSTMs. The performance of these models is evaluated and compared in terms of accuracy and computational speed. The results demonstrate that addressing this problem using MLP models provides the best performance for short-term predictions, while for predictions more than 90 min ahead, GRU results in the highest accuracy. It is also demonstrated that the accuracy of the deep learning models can be approximated as a function of the occupancy index with an MAE of 0.014.

Research on the impact of occupant behavior (OB) on energy use at various spatiotemporal resolutions in the district/community/urban scale remains limited and should be explored for district operational management and energy system optimization. This paper focuses on the inter-building occupancy on the district scale and conducts a case study for a campus in the suburbs of Shanghai. Using geographic information system (GIS)-based Time Use Survey (TUS) data from hundreds of sample students, five inter-building movement patterns are extracted by K-means clustering, corresponding to five study-life styles of students in the selected campus. The demographic of study-life styles demonstrates that the diversity of inter-building movement is considerable on the campus scale and identical schedules suggested by current domestic building design standards need to be improved and expanded.

Halloysite nanotubes (HNTs) have become one kind of promising supporting material for composite phase change materials (PCMs). In the present study, a novel solid–solid phase change material (SSPCM) was prepared based on polyethylene glycol as the soft segment and HNTs as the rigid segment through the two-step polymerization method. The prepared SSPCMs possess a steady crosslinking structure, and the SSPCMs own good crystallization properties. The SSPCM (PEG, 86.5 wt%) has high phase change enthalpy (tm is 56.49 °C, ΔHendo is 149.0 J/g) and good crystallinity of 89.1%. As a result, a two-dimensional coupled heat transfer model is established according to the floor heating system with phase change thermal storage. Comparison between the effects of SSPCM1 and cement on indoor temperatures shows that the air temperature fluctuation in the cavity with SSPCM1 structure is in a smaller magnitude, and the temperature control system is relatively simple. It can be revealed that the synthesized SSPCM can effectively save energy while meeting the heating requirements.

Fifth Generation District Heating and Cooling (5GDHC) networks, also called bidirectional low-temperature district energy systems, is a promising strategy that is more efficient for districts with simultaneous cooling and heating demands. This study presents an economic life cycle assessment of bidirectional low-temperature district systems with varying shares of different energy sources. Solar thermal energy and waste heat from the cooling demand in summer can be transferred to the geothermal energy storage to be reused by heat pumps during winter. The capital investment of the heat pumps, geothermal boreholes and photovoltaic/thermal (PVT) systems can be paid back at the end of the lifetime of the project. Additionally, geothermal systems can increase the recovery of the waste heat and solar energy.

Many software have been developed to analyze buildings and renewable energy systems. Generally, more than one software is used for the visual interface, energy simulation, optimization, and modeling of renewable energy systems in energy analysis studies. In this study, a novel web-based green building energy modeling software with layered architecture is developed to estimate the energy demand of buildings, make passive house analyses, and model renewable energy systems in buildings. Requests and responses between the user and application programming interface communicate using JavaScript Object Notation format. For the web frontend, libraries with visualization are used together with React, which is a JavaScript library created by Facebook. The developed software is free, open source and has it own visual interface. The developed software eliminates the requirement for more than one software in the analysis of standard/passive buildings with low/zero energy consumption. The developed software was verified using electricity consumption and production data of a positive energy passive building located in Turkey.

In Canada, residential space conditioning accounts for nearly 11% of all greenhouse gas (GHG) emissions. Air conditioning alone produces large amounts of GHG emissions due to majority of their operation occurring during peak electrical demand periods, which is commonly when gas burning power plants ramp up to match the additional demand. In this paper a solar adsorption system is analysed for its ability to use solar thermal energy as a source for adsorption cooling and operate as a heat pump to greatly reduce the GHG emissions from space heating. A TRNSYS model was created using an experimentally validated adsorption model and a house model representative of a typical, single-family home in Canada. Simulations comparing the GHG emissions as a result of typical HVAC equipment, including electric resistance heating, natural gas fired furnaces, and heat pumps, were performed for locations across Canada. The largest possible decrease of GHG was found in Toronto, where switching to an adsorption system from natural gas heating with a vapor compression air conditioner, would reduce the lifetime GHG by 90%.

The lack of building-integrated photovoltaic (BIPV) standards and design guidelines for architects and engineers in Canada is one of the barriers to the market uptake of this technology. This study provides key design considerations of a BIPV roof under two different configurations: naturally ventilated and with thermal energy heat recovery (BIPVT). An existing method to estimate the array temperature of infrared (IR) images was first validated. Then, the performance of King’s model to estimate the temperature of the naturally ventilated configuration was evaluated and found to provide a good estimate of the actual temperature measurements. Finally, maximum temperatures of 65 °C were observed, showing that the naturally ventilated BIPV design is sufficient to maintain BIPV temperatures below the maximum rating limit of 85 °C.

Due to increased electrification and renewable generation, electricity consumption alone is no longer sufficient to assess the performance and capabilities of heating, ventilation, and air-conditioning systems; flexibility potential also needs to be considered. This paper compares the electricity consumption and flexibility potential of four different space heating systems in a Canadian home. Existing flexibility assessment methods usually capture the flexibility of a singular system with the addition of advanced control schemes rather than compare the flexibility potential between various systems, a new method was developed. As such, this paper utilizes two different methods to evaluate the flexibility of multiple electric space heating systems using different reference systems for comparison. The results show that lower-efficiency systems, such as electric baseboards, have higher electricity consumption and flexibility at low ambient temperatures, with existing flexibility metrics. However, the proposed flexibility method, using a common reference case for all scenarios, showed a higher flexibility potential for high-efficiency systems with storage.

In alignment with climate targets worldwide, the use of latent energy storage in the form of phase change materials (PCMs) provides a means of lowering building energy loads and corresponding carbon emissions within the residential sector. The high embodied carbon typically associated with traditional PCMs, which are paraffin based, can further be reduced using plant-based PCMs. This study investigates the thermophysical properties of four plant-based oils: coconut, shea olein, palm kernel, and babassu oils, to predict their effectiveness as renewable alternatives to paraffin-based options for building integration. Three experimental apparatuses were used to evaluate the PCMs: a differential scanning calorimeter, a convective analysis tool, and a guarded hot plate. Based on their melting temperatures of 24.0 and 23.3 °C and latent heat capacities of 89.8 J/g and 94.2 J/g, coconut oil and babassu oil, respectively, are promising options to pursue for building integration.

A novel approach for demand side management (DSM) in urban areas was introduced previously based on collective intelligence (CI), called CI-DSM. It was shown that CI-DSM can enhance energy flexibility and climate resilience in urban areas while having a simple communication logic and light computation. The communication signal (0 or 1) is (gradually) distributed among the agents in the energy system, including buildings. Therefore, the communication logic can affect the performance of the whole system, especially during extreme weather events. This work investigates the impact of two communication logics on the energy performance of buildings during an extreme cold January in a hypothetical neighborhood in Stockholm. According to the results, the selected communication logic does not affect total energy demand considerably, however the impacts are visible on the indoor thermal comfort, especially during very cold hours.

Low-temperature heating (LTH) and high-temperature cooling (HTC) systems that use less energy than traditional systems are viable alternatives for dealing with rising energy demand and increasing renewable energy’s share in the global energy matrix. The present work proposes an innovative LTH-HTC system comprising photovoltaic thermal cooling panels, heat pump, and smart thermal energy storage (TES). MATLAB software evaluates the hourly variation of techno-environmental metrics to investigate the proposed system’s feasibility for a case study building in Stockholm. Moreover, the impact of key operational factors on system performance is evaluated by looking at their impact on the performance and environmental facets. According to the results, the primary energy saving and emission mitigation ratios of 54.8 and 57.2% are obtained by applying an intelligent control strategy to the TES. The parametric study results indicate that while the increase of panel area is techno-environmentally suitable, a lower heat pump capacity should be selected.

Electrification is rising rapidly in Quebec due to the increasing use of electric heating systems in buildings and emerging uses such as electric vehicles. This pressure leads to higher demand and potential degradation of the network’s carbon footprint. Thus, first an approach to model the real-time temporal carbon footprint of electricity distributed in Quebec is proposed. It is built on the programs developed by the Open-Source project electricitymap.org and uses open data published, among others, by Hydro-Quebec, the main electricity provider in Quebec. Second, this approach is used to calculate and compare the temporal carbon footprint of a generic building’s heating system using 100% electricity and 100% natural gas as well as dual-energy approach.

The objective of the current study is to assess the technical feasibility of implementing an urban-scale, solar-driven district energy system that utilizes a two-pipe, ambient-temperature ring topology for building space heating and cooling. The system model is constructed in the MATLAB-Simulink® environment and a simulation is conducted for a period of one year across a reduced two-building network. Results show that the system can achieve a maximum monthly coefficient of performance of 3.68. Annual solar fractions of 1.5% and 4.0% were found for heating and cooling, and a total CO2 − e emissions reduction of 44% was found relative to a conventional system comprising individual building-based boilers and air conditioners. The current study shows that the proposed district energy system (DES) topology is a promising alternative from both a technical and environmental standpoint. This work is a first step towards the development of a larger-scale DES model for topology assessment in comparison to a conventional DES.

The objective of this paper is to propose a centralized solar and biogas combined heating system (SBCHS) applied in rural areas, and to carry out thermodynamic analysis. The thermodynamic model of the SBCHS is established by MATLAB/Simulink, and the energy and exergy flow process of the system are analyzed. The simulation results indicate that the energy efficiency of the SBCHS is 65.4%. The solar fraction is 49.8 %, and the biogas subsystem accounted for 41.3 %. The analysis of the energy and exergy flows shows that a large fraction of the energy losses, mainly in the form of heat loss, occur at the solar collector and biogas boiler. Energy cascade utilization is the key to improve system energy efficiency. Compared with the single heating system, the SBCHS has better thermodynamic performance and economic benefits. The SBCHS provides a promising way for the effective utilization of centralized renewable energy.

Gauging information on solar gains is crucial for revealing the dynamics of indoor heat balance in buildings. Without precise estimation of solar gain dynamics, uncertainties in estimation and prediction , such as in model predictive control and fault detection, will be enlarged. In previous studies, incorporating in-situ data, a B-splines integrated grey-box modelling technique was proposed to estimate the dynamic solar gain. This technique has demonstrated potential in a simplified case, involving one thermal zone (i.e., wellmixed indoor air) and a single on/off heating input value. This study further examines the robustness of this technique in a more realistic setting, comprising two thermal zones with complex artificial occupancy profiles. A portable site office (PSO) measuring 9 * 3 * 3 m3, with two main test rooms separated by a small entrance foyer, is the study case. The two test rooms represent the living room and bedroom in a building, in which varying daily occupancy scenes are implemented. Technically, a heating system with infrared lamps of different powers is used to mimic the metabolic heat gains of the inhabitants, with on/off profiles aligned with two occupants. The study reveals the applicability of the aforementioned B-splines integrated grey-box model in this more realistic case (i.e., two thermal zones with the increased complexity of heating input) for revealing solar gain dynamics. Future studies could be conducted on real-size buildings with synthetic occupancy or real inhabitants.

This research considers open low-rise residential buildings in two of Canada’s Building Climate Zones, Vancouver and Toronto. Each Climate Zone has different weather conditions and vegetation, which lead to different building code requirements. Inputting these requirements and weather conditions into the Vertical City Weather Generator (VCWG) model, one can determine what energy saving solutions should be incorporated into residential buildings for each Climate Zone, while also making the building more affordable and reducing CO2e emissions. The results of a 30-year analysis show that increasing vegetation from one to two trees, decreasing infiltration from 1.2 to 0.4 Air Changes per Hour (ACH), and incorporating a photovoltaic system that covers 69% of the residential roof may lead to cost savings of 37.3 and 36.6% for Vancouver and Toronto, respectively. CO2e emissions savings could be reduced to 257 and 204 [Tonnes] for Vancouver and Toronto, respectively, if an R-value increase was also incorporated into the most cost-efficient case. However, this would reduce total cost savings.

Solar-assisted air source heat pumps (SA-ASHPs) are an emerging research area. This paper presents a review of the available literature on SA-ASHPs, with a special focus on cold-climate applications; to the authors’ knowledge, this paper is the only existent literature review focusing solely on SA-ASHPs. Twenty-five (25) relevant papers are identified, of which eleven (11) involve experiments or simulations conducted in cold climates. The available literature shows that SA-ASHP systems routinely achieve COPs above 2.0 under experimental conditions, and suggests that such systems are a viable alternative for space heating, space cooling, and DHW production in hot, cold, and temperate climates. Additionally, SA-ASHP systems are categorized as parallel, semi-parallel, series, or combined based on system topology. No single system topology consistently outperforms the others.

Many Arctic communities, such as those from the Nunavik (Quebec, Canada), are not connected to the main power grid and rely on costly and polluting diesel power plants. Understanding electricity consumption behaviors and patterns is crucial to develop energy storage solutions and integrate intermittent renewable energy sources in the mix. However, there is currently a lack of statistics and data available on the electricity consumption of houses in these communities. In this work, the electricity consumption of 12 units in Quaqtaq (Nunavik) have been monitored in detail for a year and an analysis of the consumption patterns is presented. Results show seasonal patterns for dominant loads and significant variations among units with an average daily consumption of 14.31 and 10.13 k Wh for the dwellings and mechanical rooms, respectively. This study provides key information that can ultimately be used for numerical energy simulation and optimization of northern buildings and microgrids.

Research studies previously performed using a singular climate had shown that certain solar shading devices have the potential for energy savings for buildings. In this paper, a commercial building was studied using the DesignBuilder programme to identify the solar performance of the building. The model represented a typical commercial building with standard properties. This study allowed for comparison for horizontal louvre, overhang and vertical shading types in various climates (Florida, Cairo, Leeds and Reykjavik) for a year presented in terms of monthly energy demand. Comparative analysis identified that the horizontal louvre type, which consisted of 5-louvre with 400 mm spacing between the adjacent louvres, performed best to reduce the annual energy usage for Florida (4191 kWh), Cairo (5194 kWh), Leeds (1882 kWh) and Reykjavik (957 kWh). In general, all environments showed increases through heating and lighting, but proportionally cooling reduction was higher annually.

In this paper, a novel centralized solar heating system coupled with a water-water heat pump (Coupled-CSHS) is proposed to improve the heat collection efficiency of solar collector field (SCF). A residential building in Lhasa is selected as the research object, and a system model is established through TRNSYS for analysis. The results show that when the SCF area is 80 m2, the Coupled-CSHS can improve the heat collection efficiency by more than 10% and the solar fraction by 13.9%. However, as the area of SCF increases, the advantages of Coupled-CSHS in heat collection efficiency and solar fraction are no longer significant. Therefore, when designing the solar fraction of the Coupled-CSHS, the solar fraction should be less than 80%. At this time, the heat collection efficiency can be increased by at least 5%, and the SCF area can be reduced by more than 6.3%.

The spectrum of skies in the world was classified into a range of 15 standard skies. These standard skies are crucial in estimating solar irradiance and daylight illuminance needed for energy-efficient building designs. Generally, using the sky luminance distributions to identify the standard skies is the most effective method, but these are sparingly measured. Alternatively, climatic variables can identify the standard skies. Nevertheless, it is necessary to determine if the available climatic variables could correctly identify these skies. Also, there are several climatic variables, but there is no criterion for selecting a climatic variable over the other. This study addresses the lack of luminance distributions measurement by classifying the standard skies using measured climatic data. Furthermore, sensitivity analysis was used to determine the relative importance of one variable over the other. Importantly, the proposed approach for classifying the standard skies was implemented using support vector machines (SVM). Findings showed that the SVM could classify the skies with an accuracy of 72.4% on the training data and 71.4% on the test data.

This paper simulates the thermal response of a single-family house to power outages during extremely-cold periods, using a multi-zone data-driven energy model. The archetype model is developed with data from an unoccupied research house at Laboratoire des Technologies de l’Energie, Québec. Indoor air temperature is first simulated, assuming no auxiliary heating during an outage. Next, to explore the impact of adopting technologies on the building performance during this period, the simulation is repeated, assuming a residential-size PV/battery system feeds either baseboard heaters or a heat pump with a rule-based control. The free-floating indoor air temperature rapidly deviates from comfort conditions; however, with auxiliary heat from the heat pump, the comfort conditions are retained throughout the outage, emphasizing how integrating PV/battery plus heat pump boosts the building energy resilience.

This paper examines the building energy flexibility capabilities—the ability to module load—of a retrofitted façade-integrated photovoltaic/thermal-energy storage system for radiant heating in winter conditions. The optimal system configuration is determined as a function of thermal storage size to reduce peak demand for electricity during the grid peak periods in the morning and evening in Quebec. A reference unoptimized case and optimal case scenarios are evaluated to determine the system performance and potential energy flexibility that can be provided to the grid by reducing the energy consumption relative to the reference case. With optimal energy storage sizing and optimal PV/T orientation, the system was found to achieve a reduction of the peak load of 98.40% for the morning load and 84.19% for the evening load under perfect weather conditions. The study found that energy storage sizing had greater impact on the PV/T-energy storage system potential energy flexibility compared to PV/T orientation.

As global efforts on the electrification of the built environment intensify, buildings are expected to become energy-flexible and provide different services to the grid, including onsite electricity generation. Distributed energy sources such as building-integrated photovoltaics (BIPV) have a key role to play in this transition. Thus, it is essential for building professionals to use accurate and reliable tools to predict the energy performance of these installations. BIPV refers to photovoltaic systems that can replace conventional building components. The electrical performance of BIPV can differ from that of conventional PV due to their tilt angles, poorer rear ventilation, and partial shading by the surroundings. The objective of this study is to review existing performance models and evaluate their applicability to BIPV. In this study, the performance of a 5.3 kWp south-east facing BIPV rainscreen system of an institutional building was evaluated for one year on the basis of monitored data. High-resolution meteorological, electrical, and surface temperature data were collected and analysed. A model was created based on the BIPV system specifications using the NREL System Advisor Model (SAM). The analysis revealed that SAM can generate accurate and reliable data when it comes to predicting the electricity-generating and temperature behaviour of a BIPV façade, with an overall coefficient of determination (R2) ranging from 48 to 95%, depending on the simulation timestep used.

Buildings consume more than one-third of the world's primary energy. Therefore, as the population grows, so will the demand for energy. There are two approaches to this scenario: generate more energy in a clean and renewable way or reduce energy consumption in buildings through an energy efficiency strategy. The key question is how far energy efficiency measures will go in terms of economic benefits. In this context, the general objective of the study is to investigate the balance between reducing energy consumption and generating photovoltaic energy in single-family homes of social interest. The consumption avoided by energy efficiency measures was used to size the photovoltaic systems, so that the performance and economic profitability of both systems were compared, leading to a reduction in energy demand and the generation of clean energy. The results show that efficiency measures are not economically efficient compared to photovoltaic systems. In all assumed scenarios, photovoltaic systems are better from a financial point of view.

This research aimed to design a controllable multi-inlet Photovoltaic/thermal (PV/T) system with dampers to control system operation. For this purpose, five openings and two dampers were employed on a module of PV/T system. The developed steady-state numerical two-dimensional model of the proposed system is applied to evaluate three study models, one one-inlet system, and two two-inlet systems, on the 15th of February, in Montreal to assess thermal generation and temperature. The results show that one-inlet system could have further thermal generation for high air velocity. The two-inlet systems can perform better than the one-inlet system in low air velocity, but the thermal generation difference decreases with increasing air velocity.

Incorporation of optimal passive parameters at an early design stage is essential for maximizing energy savings while maintaining occupant comfort. Current study involves proposing a methodology for building simulation optimization utilizing admittance method combined with optimization frameworks for air-conditioned buildings located in Delhi, based on optimal envelope parameters for enhanced thermal performance. Features such as orientation, aspect ratio, wall and roof constructions, window systems, etc., have been taken into consideration. To investigate the efficacy of different metaheuristic algorithms in providing optimal envelope performance, a comparative study between genetic algorithm and grasshopper optimization algorithm has been carried out for the first time in such application. Results suggest that the latter technique outperforms the former. The present work also studies the change in optimal parameters and thermal performance between buildings with 24-h occupancy and ones with 8-h occupancy. The results have been well-validated with standard test function and Design Builder software.

In this study, a BIPV/T system coupled with heat pump is proposed where the heat extracted from the BIPV is used to drive the heat pump. The energy saving potential is assessed for a case where the heat extracted is used for domestic hot water heating. An analytical model of the BIPV/T concept is developed using CFD and implemented in EnergyPlus to assess the energy saving potential. Energy savings of up to 40.1% is possible for the BIPV/T façade concept mounted on the roof in Vancouver, BC.

This study analyzes energy modelling developed for a net zero energy (NZE) home, complete with space heating/cooling, domestic hot water (DHW) heating, appliance/lighting, mechanical ventilation and electric-vehicle (EV) battery charging for a local commute. The house heat loss and cooling load demand was evaluated against annual load targets and the model was determined to be 97% accurate. Photovoltaics (PV) energy production was introduced and a 3.7% nominal deviation in net energy distribution was found over summer and winter solstice days when compared to a similar study. Lastly, a control study was conducted to evaluate the energy performance achieved by implementing a rule-based (RB) control strategy, with traditional optimization, based on peak-load shedding, greenhouse gas (GHG) emissions and time-of-use (TOU) energy cost (EC). The results obtained from the altered energy consumption profiles showed a 79.8% and 21.5% improvement in load shedding, 2.4% and 7.6% increase in GHG emissions and 8.4% and 61.1% reduction in EC for heating and cooling seasons respectively.

Identifying solar design strategies can lead to understanding sustainable design principles and prioritizing energy efficiency measures. The study analyses the energy performance of an ancient vernacular architecture in the composite climate of Delhi, for its passive solar strategies, and develops a MATLAB code to assess its viability. This would provide inputs to the architects to incorporate such features into modern buildings from the first stage of building design (concept visualization) and save time and energy to be spent on selecting efficient active techniques. Solar radiation, illuminance, ambient temperature, and building’s temperature have been measured to assess annual energy savings. The energy matrices (embodied energy, energy payback time, energy production factor and life-cycle conversion efficiency) and enviro-economics (CO2 emissions, net CO2 mitigation and carbon credit) have been evaluated, along with a parametric study of wall thickness acting as ‘Trombe’ wall, building’s life, and solar radiation intensity. Results suggest that significant energy savings and other benefits are possible from adopting ancient vernacular building’s passive strategies.

Dynamic simulation along with soil sensitivity analysis was conducted to examine how specific extreme soils affected the overall heat transfer performance of borehole heat exchanger. Effects of various ranges of soils with extreme properties were numerically tested through changing the borehole heat exchanger parameters such as shank spacing, grout thermal conductivity, and borehole length. The soils tested were chosen based on their extreme thermal conductivities. Through the performed simulations, it was determined that, through increased soil thermal conductivity and decreased grout thermal conductivity, borehole loading (in terms of Watts per unit borehole length, W/m) would be maximized. Similarly, shorter total borehole depths would also increase the Watt’s per meter length. Increasing the half shank spacing would also in turn increase the borehole loading. Through the analysis of various types of soils used for both single and double U-tube borehole heat exchangers (BHEs), an optimized design can be found through ensuring which specified parameters are either maximized or minimized.

The objective of this paper is to present a methodology for improving the energy efficiency and energy flexibility of thermal zones with triple glazed semi-transparent photovoltaic (STPV) windows, controlled motorized Venetian blinds (MVB) and hydronic radiant floor heating (HFH), while maintaining thermal comfort for the occupants. Two blind tilt angle strategies are used to control the solar gains allowed into the zone, while optimal temperature setpoint profiles are chosen based on one-day ahead weather predictions with an explicit finite difference thermal network model. These setpoint control strategies were simulated to assess the building energy flexibility of the zone load profile relative to the reference load profile. The setpoint strategies effectively shifted the heating load outside the peak demand periods of the grid in Quebec. The BEFI was calculated as 100%, when switching from a reference to an optimal temperature setpoint for always open blinds, during both peak periods on cold sunny days.

A key factor for successful Building Integrated Photovoltaic/Thermal Systems (BIPV/T)—HVAC integration is optimal control of the flow rate in BIPV/T systems to achieve either a desired outlet temperature or maximum thermal efficiency. This work presents a novel approach using model predictive control for controlling the air velocity within an air-based BIPV/T system coupled with an Air Source Heat Pump and an Energy Recovery Ventilator. A control-oriented grey-box BIPV/T model is developed and calibrated using monitored data from a full-scale BIPV/T system. A predictive controller is utilized to minimize the energy consumption of the building by regulating the BIPV/T flow rate and therefore determining the ideal mixing ratio with unheated fresh air sent to each thermal application. A reduction of 21% in power consumption is achieved compared to a base case scenario.

This paper discusses thoroughly the regulatory design provisions of the current wind standards and codes of practice and their comprehensive scope for structural wind resilience of various photovoltaic systems, namely Building Attached Photovoltaics (BAPVs) on roofs or walls and Building-Integrated Photovoltaics (BIPVs) into building envelope or components. Energy resilience and structural resilience, which are always at the forefront of general building resilience, are closely linked. Structural resilience is concerned with the performance, serviceability, and functionality of any photovoltaic system fitted into the building or providing the building with energy. Ensuring structural resilience would significantly enhance the energy self-efficiency of the building; and thereafter, the energy resilience of the building. By surveying the set of provisions available in the current wind codes and standards, the paper stresses the need for substantial research progress to be made toward the evaluation of wind loads on PV modules at different installations.

This study evaluates the feasibility of integrating solar energy into high-rise commercial buildings by measuring its effectiveness in reducing their external energy needs and operating greenhouse gas emissions. To exploit the solar potential, all the available areas on the roof and façade of an archetype high-rise building, located in Toronto, are covered with different combinations of photovoltaic, thermal, and photovoltaic-thermal collectors and an alternative cooling system, i.e. desiccant cooling using excess solar heat during the summer, is implemented. The results indicate that exploiting solar energy potential can cover 11–25% of the total energy demand of the building and reduce its emissions by 4–38%. Converting solar energy to heat, either through a thermal or a thermal-photovoltaic collector, is shown to be more effective in reducing both building dependence on the energy grid and operating emissions. Thermal technologies yield higher solar energy coverage because they have higher efficiency compared to photovoltaic panels. Given that natural gas has a 5.6-time higher carbon intensity compared to electricity in the city of Toronto (31 vs. 175 gCO2e/kWh), thermal harvesting technologies are more effective in lowering the emissions. This conclusion is, however, not universal, as in many locations, electricity can have a higher carbon intensity compared to natural gas.

The building sector demands energy to service occupant comfort and amenity. Thermal energy is sustainably serviced to the built environment via efficient mechanical heating, ventilation, and air conditioning (HVAC) and plumbing systems. Efficient mechanical HVAC/plumbing systems exploit thermodynamics and energy conversions. Mechanical systems synchronized with the natural environment foster sustainability in their effort to effectively source and service thermal energy. Insight on mechanical system feasibility will provide justification of their future implementation. Performance, application, and limitations of mechanical solutions will be analysed for selection. Energy harvesting/storage/recovery can achieve significant merits to our people, planet, and profit. Reviewed distributed heat pump system heat recovery for a case study building with simultaneous heating and cooling demands resulted in a 17% reduction of thermal energy consumption.

Heating, cooling and electrifying Canadian households emitted 65 million tons of carbon dioxide equivalent (CO2 eq.) in 2018 or 12% of total greenhouse gas emissions in Canada. In the solar-dark Northern winters, space heating is the largest driver of residential energy use. Pyrogenic carbon capture and storage (PyCCS) from residual and forest biomass has the potential to make space heating carbon-negative across this region. PyCCS generates both heat that can substitute fossil-based counterparts and biochar, which improves soil carbon storage and plant productivity. Evidence shows that biochar application increases biomass productivity by enhancing nutrient uptake and water use; PyCCS is a potential win–win for emissions and crop production in the Sub-Arctic. Although existing PyCCS projects in Finland, Sweden, Germany, and Norway show the technology to be both profitable and effective at eliminating emissions, there is a lack of studies on the effectiveness of PyCCS in reducing emissions in the Northern Canadian context. We performed a carbon and economic analyses of a hypothetical PyCCS system that utilizes imported wood pellets, and locally-harvested fire-killed trees as feedstocks and compared these to conventional heating systems using fossil fuels and wood-pellets in Yellowknife, Northwest Territories. We found that emissions per unit of energy delivered to a household using PyCCS were substantially lower (−191.1 and −193.3 kg CO2 eq. per kWh for locally harvested fire-killed trees and imported wood pellets, respectively) than those generated from residential heating oil (341.0 kg CO2 eq. per kWh), gas (293.4 kg CO2 eq. per kWh), and wood pellets (46.0 kg CO2 eq. per kWh). The forecasted increase in the price of carbon at the national level as well as the various federal grants supporting a low-carbon economy and CCS will make PyCCS competitive with conventional systems. Future work should focus on better incorporating biogenic carbon cycles into the carbon footprint analysis and improving the financial analysis using data on PyCCS operations and carbon pricing.

Building integrated photovoltaic (BIPV) systems are being developed for exterior wall construction, but the effect of BIPV as a replacement for conventional cladding on durability must be considered. This study compares the hygrothermal performance of a low-rise residential wood-frame wall with different options for exterior cladding. These options include fiber cement and BIPV in both the open circuit (OC) operating state and maximum power point tracking (MPP) state. Hygrothermal simulations of each case were performed using WUFI Pro 6. Parameters examined include relative humidity (RH), moisture content (MC), and mould growth index ( $$M$$ M ) of critical layers in the wall assembly, the oriented strand board (OSB) and the wall assembly air cavity. The results show that, under low ventilation rates within the air cavity with rain leakage assumed, the BIPV cladding options have worse hygrothermal performance compared to fiber cement cladding due to solar-driven moisture accumulation and impermeable cladding surface. The BIPV clad wall assembly under MPP state performs slightly better than when under OC state. These results suggest that a minimum of 20 ACH cavity ventilation rate is required in maintaining hygrothermal performance of wood-frame construction when adding BIPV cladding, which is typically not a concern since the ventilation rate of naturally ventilated BIPV systems is in the range of 200ACH and greater than 600 ACH in mechanically ventilated BIPV/T systems.

Collective intelligence (CI) in demand-side management (DSM) can enhance the flexibility of urban energy systems. Extreme climates cause intensively high loads on the urban energy systems resulting in power outages. To avoid this, quick responses are needed from buildings to adjust their operation in favor of the grid. Most of the available approaches are computationally expensive. CI-DSM offers a simpler approach that relies on distributed intelligence paradigm. It allows fast and (semi-) autonomous reactions to the continuously changing environment. This research investigates the application of CI-DSM in a residential building in the south of Sweden. The focus of the study is managing the building’s heating demand in an extremely cold winter. Heating setpoint and ventilation rate are defined as the adaptation measures. To activate the system and take an action by the agents, signals of 0/1 with 15-min intervals are sent, when heating demand exceeds the baseline. Managing the performance of buildings using CI-DSM could reduce the heating demand and peak power by 25% and 20%, respectively, over an extreme cold February compared to typical conditions.

The U.S. Department of Energy Zero Energy Ready Home program has been nudging builders toward net zero since 2013 and > 9700 homes are now certified. Measures chosen by builders to hit net zero with all-electric homes include framed 2×6 24 inch on center or SIP walls and central heat pumps. Builders achieved DOE ZERH certification at an average added cost of $10,500 without PV or $25,000 with PV over code homes. Many all-electric homes achieved net zero with a net gain to homeowners where the added cost to build a ZERH is outweighed by the monthly energy savings; in some cases the net gain is > $200/month over a 30-year mortgage. A calculated example of PV payback in cloudy Burlington, Vermont, USA, shows for a 2000-ft2 home with an oversized PV system and no credits, the 10.5-kW PV + 28.8-kWh battery pays for itself in just 19.4 years.

Lignocellulosic materials from different wood plants could be utilized as value added insulation materials manufacturing through reinforcing with ordinary Portland cement (OPC) and Montmorillonite. The developed products considered as green composite materials as produced from the green and renewable plant-based reinforcements which ensures less emissions of greenhouse gases like CO2. Typically, cements contain higher thermal conductivity values compared to the lignocellulosic materials. Therefore, the thermal conductivity values of the developed panels started to increase with the decline in fibers and increase in cements. The perceived thermal conductivity values are 0.11 (0.01)–0.13 (0.013) W/(m K). Moreover, the morphological studies also displayed an excellent interaction between the fibers and cement matrix in the composite system. Therefore, the fibers are uniformly distributed in the cementitious composite system as seen by SEM (Scanning electron microscopy) images. The FTIR analysis also providing a successful reinforcement effect between the fibers and the OPC matrix. Internal bonding strength of the composites also shown significant mechanical properties where the highest value was shown by panel 3, where 3.5 proportion cements were used (0.22 (0.08) MPa). Furthermore, dimensional stability also found satisfactory which was tested in terms of moisture content. In summary, a biomass-based sustainable green composite production route is found which provided superior insulation properties. The developed products could show a novel milestone in construction and building industry.

Thin films made of metallic nanoparticles can exhibit strong photothermal effects on near-infrared light irradiation and also show unique semi-directional heat transport features due to quasi-ballistic thermal transport from the localized surface hot spots. In this work, we incorporate this feature into a new design on greenhouse coverage. The thin films were placed on the interior surface in winter and outside in summer relative to the greenhouse coverage layer. The simulation results show that the different placements of the thin films present a substantial ability to control solar heat gain with a range between about 0.2 and 0.6 with a stable Photosynthetically active radiation (PAR) transmittance of 0.32. The variation of solar heat gains of the system may meet the operational energy saving needs without compensating solar radiation for the plant growth.

Summer sol–air temperature (tsa) is a basic parameter required for building cooling load calculation, which is a comprehensive performance of heat radiation and outdoor air temperature. And the value is determined by the total heat transfer coefficient (αw) on the wall exterior surface. The code for thermal design of China stipulates that αw equals 19 W/m2·℃, which ignores the correction on convective heat transfer coefficient (αc) affected by prevalent wind speed in different climate zones and the evaporation heat transfer coefficient (αv) caused by rainfall. To address this issue, a case study was conducted on an island building in China. The value of αc at high wind speed (4.2 m/s) was corrected, and the hourly value of αv on the humid wall surface was supplemented. The results show that due to the correction of αw, tsa decreases by an average of 3.35 ℃ on the vertical and 8.34 ℃ on the horizontal.

Smart control of window is a means of effectively reducing concentrations of indoor PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) in naturally ventilated residential buildings without air cleaning devices. This study aimed to develop a reinforcement learning (RL) approach to automatically control window behavior in real time for mitigation of indoor PM2.5 pollution. The method trains the window controller using deep Q-network (DQN) in a specific apartment for a month. The trained controller can be employed to control window behavior to mitigate indoor PM2.5 in that apartment. The input data for the controller are the real-time indoor and outdoor PM2.5 concentrations with 1-min resolution. Simulation was conducted in a real apartment in Tianjin. The results show that, the RL algorithm reduced the average indoor PM2.5 concentration by 9.11% when compared with the I/O ratio algorithm and by 7.40% when compared with real window behavior.

This paper explores the feasibility and strategies of using model-free reinforcement learning-based control (RLC) for the slow response radiant floor heating (RFH) systems with a setback setting. First, a detailed physics-based virtual testbed is developed and validated. Then based on the virtual testbed, four different strategies of RLC to handle the slow response are studied, along with a conventional rule-based control (RBC) without setback as a baseline and an MPC with a setback for the upper bound on the performance. The results show that the DQN_TD(λ) with forecasted weather data as states provides the best performance, showing potential for applications. Compared to the baseline, the heating demand is reduced by 19.1% with RLC and 18.5% with MPC. The unmet hours of RLC with our settings are higher than that of MPC, which suggests that more research is needed for RLC to better meet the constraints.

Small buildings make up a majority of the existing commercial and industrial building stock but have historically been challenging to upgrade and study. The advent of low-cost sensing and control from smart thermostats has begun to address these challenges. However, the effect of the thermostat installation and the associated centralized management has not been well investigated. To this end, we leveraged a unique data set from a portfolio of over 400 small commercial buildings which had undergone thermostat upgrades. We used the data to compare the energy use intensity of these buildings to other benchmark values and study the energy and environmental impacts of the thermostat upgrades. We found that this portfolio’s energy use is similar to other reported values but caveats need to be considered. Ultimately, we determined this portfolio achieved savings of 18% and 19% for natural gas and electricity usage, respectively.

Submetering significantly enhances building performance monitoring capabilities by providing a higher resolution to resource use and revealing energy consumption across the building and systems that would otherwise remain hidden. Previous studies indicated that building automation system (BAS) trend data represents an untapped opportunity to disaggregate existing submeter data for heating, cooling, and electricity into zone- and system-level end-uses. Algorithms can disaggregate bulk meter data by looking at trend data that provide contextual information regarding the operating status of energy-consuming equipment. However, the level of submetering required to enable end-use disaggregation has yet to be studied. To this end, this paper investigates the effect of submeter density and configuration on the performance of a regression-based disaggregation strategy using BAS trend data as predictors. The method was evaluated using synthetic meter and heating and reheat coil valve and perimeter heating device status trend data generated through a building performance simulation (BPS) model of a government office building in Ottawa, Canada. The results indicate the minimum number of heating energy submeters needed to be installed in a building for accurate zone- and system-level disaggregation. The methodology presented in our paper can also inform changes in building design codes and standards regarding the minimum density and appropriate configuration of the metering.

It is established centralized energy management (CEM) approaches can significantly improve the energy efficiency of heating, ventilation, and air conditioning (HVAC) systems. Regardless of the energy-saving potential, the CEM is rarely implemented in the building because of the difficulties in the development of a reasonable performance model for the synthesis of CEM and computational complexity due to the centralized architecture of CEM that also limits the scalability of CEM [with addition or removal of components]. Furthermore, centralized CEM for large-scale applications requires large amounts of data from local control loops to be sent to building management systems (BMS) to update the state of the system for optimal control action. This study aims to enable distributed intelligence with the development of agent-based decentralized energy management (ADEM) solution for building HVAC systems by systematically addressing issues with CEM. A performance model was incorporated following agent-based distributed optimal control requirements. Followed by the decomposition of the HVAC system into subsystems for the realization of agent-based decentralized architecture. Then agents were defined for subsystems, as a uniform decision-making process for all agents for ease of understanding and implementation. The proposed ADEM approach was tested in a simulation environment on a complex HVAC system under actual operating conditions recorded from a real building. Case studies were carried out to evaluate CEM and ADEM approaches’ optimization accuracy, energy performance, and communication load.

This paper examines the impact of building zoning on demand flexibility potentials. The building thermal dynamic response is modeled by using the state-space representation method. The comfort preference of the occupants is defined by statistical analysis of set-point temperature patterns. The created models are used to study energy flexibility through a coordinated energy management problem by means of the Stackelberg game strategy. The zoning influence on flexibility potentials is analyzed by applying this procedure to a set of houses in Quebec based on two specific scenarios accounting for single and multi-zones. The flexibility potential is evaluated by comparing overall demand peak reduction and heating consumption under each scenario signifying passive heating storage capacity of buildings. The single-zone scenario performs better at harvesting energy flexibility through reducing the peak load by 33%. The results demonstrate that the building zoning definition can lead to conditions under which the demand flexibility can change.

Due to the increasing demands of energy conservation and emission reduction, the efficient control of indoor thermal environment aims at realizing thermal comfort with the least energy consumption. Taking an ultra-low energy public building as the case study, a physics-based model was established based on the measured datasets in order to provide databases for training data-driven modelling, which is a model-based predictive control (MPC) method established via neural network. After calibration, predictions of thermal comfort and loads under different temperature settings were obtained in summer and winter, based on input parameters of time, outdoor temperature and humidity, solar radiation and indoor humidity. Afterwards, hourly optimal temperature settings were recommended to take good use of the thermal inertia, and thus provide optimal references for the intelligent operation and control.

Building model predictive control (MPC) relies on a white- or grey-box model that can require significant time and domain knowledge to develop and calibrate, often on an ad hoc basis. Black-box models can be developed and trained with limited domain knowledge and are easily transferable. In this study, a deep neural network is trained to predict indoor temperature response using a few easily obtained predictors. The trained network is embedded within an MPC framework, replacing a grey-box model, and this data-driven predictive controller (DDPC) is implemented in a facility consisting of two side-by-side identical office spaces. One office is controlled by DDPC; the other remains under automated control. Experimental results show that DDPC reduces energy consumption by up to 30% compared to baseline control while maintaining indoor temperature throughout the day. DDPC presents a scalable solution to the challenges associated with developing and implementing building MPC on a large scale.

Digital Twins (DTs) are a relatively new concept in the construction industry. They usually rely on calibrated physics-based simulation models for prediction purposes. Such models are resource-intensive and therefore expensive to build and deploy. To address this, surrogate models provide a suitable alternative for DTs as these are much faster to evaluate (and hence calibrate) and potentially more flexible to deploy. This research devises a neural network-based surrogate model implementation using easily accessible building parameters and weather data. The methodology constitutes parameter mapping, time-series surrogate fitting, and output validation. The resulting output provides the time-series building energy consumption patterns for any use case. To demonstrate the applicability of this methodology, this study uses the US Department of Energy Medium Office archetype for time-series surrogate formulation. When considering electricity and natural gas consumption on a 10-min resolution, the CNN surrogate provides a better fit for electricity (R2 = 0.977) against natural gas (R2 = 0.824). Furthermore, the predictions were more accurate for natural gas predictions (RMSE = 0.094 kWh) against electricity predictions (RMSE = 2.473 kWh).

Waste management systems have always been considered complex. Scholars have studied waste management mostly from a macro perspective for a long time, considering waste management policies as a fraction of this complex system. This study presents the causal variables and feedback relationships related to waste management from another perspective, from the inside of a policy, using the macroscopic ideas of Environment-based design (EBD) and the system dynamics pictorial representation. It clarifies the way in which policy acts on waste management systems and provides a new perspective for subsequent research on waste management policy.

Data-driven energy prediction models have drawn extensive attention in building domain in recent years. Improving the predictive accuracy of energy prediction models has been the main concern for existing research. However, an accurate model could not ensure perfect performance under all situations and the performance variation may cause fairness problems. To improve the fairness in terms of having uniform predictive accuracy under different situations, this paper applied two in-processing methods for a regression model, named mean square error penalized (MSEP) regression and mean square error constrained (MSEC) regression, to predict hourly energy consumption for an apartment. The result show that MSEP and MSEC could effectively increase the accuracy similarity in terms of MSE rate to be higher than 75%. However, MSEC would decrease the NMBE from − 3.2% (for reference case) to − 14.7%, while MSEP did not show significant negative effect on predictive accuracy in terms of NMBE.

To choose a proper method of fire detection, one should take particular fire prevention requirements of massive Guan style buildings from Ming and Qing dynasties in the Palace Museum into consideration. This method should be able to detect an incipient fire and intervene original appearance of the building on a minimized level. This thesis carries out a research on Very Early Warning Aspirating Smoke Detector is superior to other types of detectors in terms of the detection of the incipient stage of fire through fire test and practice inside massive buildings of the Palace Museum. It contributes to the quick response which would be taken at the incipient stage of fire.

Fire safety during operational hours in high-rise office buildings is always a headache because of the difficulty of evacuation. The fire dynamics simulation (FDS) can provide important guidance on how the smoke from the fire may spread in the building, while to capture this transient development of smoke and airflow movement, the numerical simulation of FDS is normally very computationally extensive and time-consuming. To avoid unnecessary simulation hours for the different floors, this study suggests conducting an overall evacuation simulation before the smoke spreading simulation to keep track of the evacuation time for each floor, and this evacuation time for each floor can be used as the total simulation hours in FDS. This procedure of simulation has been demonstrated for a real high-rise office building six-storey with a footprint area of 2340 m2. It has an expected number of occupants of about 1125, two staircases on each end of the main structure and one staircase for the extended part, and six entrances/exits on the ground floor. Two behaviour modes have been considered in the evacuation simulation, including the mode specified by the Society of Fire Protection Engineers (SFPE mode) and the steering mode. After the evacuation time of each floor has been evaluated, the FDS simulation is conducted thereafter, and two fire source locations have been considered in the simulation, one in the atrium of the building and one in the corridor of the second floor. The spread of smoke on different floors have been evaluated and compared through the soot visibility, and the CO concentration at the critical evacuation times. The steps to conducting the evacuation simulation before the FDS simulations suggested in this study provide an efficient procedure for the smoke spreading evaluation in high-rise buildings.

There are fire risks in some old buildings that have been built decades ago in Taiwan because they cannot meet the requirements of the newly revised laws and regulations on buildings and fire protection. Legally open internal staircases have been set up in some old small long-term senior citizen care institutions. In the case of fire, these stairs will act as smoke distribution pipes due to the stack effect, harming senior citizens in the whole building. This study focused on risk improvement. The result shows that there was a significant stack effect in such buildings. Curtains at the stairs of 2F can delay smoke entering for 30–60 s with only a small volume of smoke, so that the visibility was increased by 20%. The improvements proposed in this paper are cheap and should be highly accepted by operators.

The heat release rate is the fundamental data in designing the fire extinguishing system, simulating fire growth and evacuation. However, due to the cost of time and money for the fire experiment, there is still no adequate data for commercial shops in the real sites. To obtain the combustion parameters and temperature distribution during the fire, a full-scale fire test of a retail shop was carried out based on the 40 MW large-scale calorimeter. Cameras and thermocouples were used to record the visual images and temperature variations. The results showed that the maximum value of heat release rate was 12.91 MW, and the maximum value of fire growth rate index was 1.2 MW/s. The characteristics of temperature distribution were analyzed. It was found that the rupture of the glass wall was indicative of the transition from the ventilation-controlled stage to the fuel-controlled stage. The result will provide more information in understanding the shop fire progress in realistic situations.

To investigate the smoke hazards associated with PV (photovoltaic) module fires and Li-ion battery fires, fire effluent data are analysed. Due to the combustible and flammable materials composing of PV and Li-ion cells, toxic chemicals as well as explosive gases are released in various concentrations from their combustion. These data should be considered in designing effective smoke detection and ventilation systems.

Indoor airborne microorganism’s transmission can be effectively mitigated using an in-duct ultraviolet germicidal irradiation (UVGI) system and adequate ventilation. However, quantifying the impacts of ventilation conditions (air temperature, velocity and humidity) on its inactivation performance is not clear, resulting in the uncertainties of a UVGI system’s performance under different ventilation conditions. Therefore, a 3D Computational Fluid Dynamics (CFD) model combined with the r method for UV lamp output prediction, radiative transport equation (RTE), and UV dose equation were introduced in this study to characterize and predict an in-duct UVGI system inactivation performance. Further, the UVGI system design from the U.S. Environmental Protection Agency (EPA 600/R-06/051) was used to illustrate the effects of varying air temperature (15–30 °C), velocity (1.5–3 m/s) and RH (20–80%), and to predict its seasonal inactivation efficiencies for the cities in North America in light of COVID-19 mitigation.

In the spring of 2020, an unprecedented global lockdown was implemented for controlling the spread of COVID-19 in many countries. In this case study, we utilized a dataset collected from a typical office building in Northern California to conduct analytics on the energy benchmarking and load shape, as well as the impact of occupancy on the energy consumption. We also investigated and compared the occupancy and energy use pattern before and during the COVID-19 pandemic. The restriction on occupancy tends to reduce the energy consumption in office buildings, particularly on lighting and plug loads. However, the public health regulations on increasing ventilation rate and extending building operations to improve indoor air quality lead to the levelized or even increased HVAC energy use for maintaining the indoor air temperature, humidity, and CO2 concentration. This case study provides an example of how rich building datasets can be analysed using data-driven approaches to understand and improve the understanding of building operations towards a better building design and a more efficient operation.

In the setting of widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) community transmission, how to mitigate the potential transmission risks in dental procedures could help the restoration of global dental services. Owing to the insufficient understanding of the droplet spatial–temporal distribution in dental procedures, the study aims to employ the laser light scattering method to investigate the emitted droplet characteristics. The ultrasonic scaling is performed in the mock-up experimental dental clinic with six air changes per hour (ACH), and the instantaneous moments of the emitted particles are recorded by a high-speed camera. The speed of emitted particles near the oral cavity could reach ab. 5 m/s, causing the high-level contaminated area within 1 m from the patients’ oral cavity. Cooperation between air purifiers and high-volume evacuation (HVE) could significantly reduce the airborne lifetime of emitted droplets. These obtained results could complement dental clinic infection control policy and practice.

The use of face masks has innovated some existing knowledge and technical standards on indoor air quality and personnel exposure. The objective of this study is to evaluate the performance of different turbulence models in predicting the characteristics of respiratory flow and dispersion through face mask. Computational fluid dynamics (CFD) technology combined with the species transport model was employed. The result shows that the viscous resistance coefficient of face mask was 3.65 × 109 and the inertial resistance coefficient was 1.687 × 106. For respiratory flow through the face mask, the simulation result of shear stress transport (SST) k–ω model was closer to that of experiment and LES model, and the average relative deviation between SST k–ω model and experimental result was -13.6%. For the concentration inside the face mask, the SST k–ω model has higher accuracy than renormalization group (RNG) k-ε model and realizable k-ε model, and the relative deviation between SST k–ω model and LES model was 1.2%. The information obtained in this study would provide the basis for the simulation of respiratory flow and dispersion through face mask.

This study focused on the impact of the reproduction of oral cavity shape on the droplets dispersion dynamics, and compared the coughing dynamics between human models with and without oral cavity by using computational fluid dyanmics (CFD) technique. In this analysis, droplets were released from mouth opening surface or from the trachea via oral cavity. The results showed these models made differences of exhaled airflow pattern, the droplets dispersion and the rate of inhalation and deposition depending on the oral cavity shape. In conclusion, the droplets dispersion dynamics with consideration of oral cavity shape might contribute to accurately predict the dispersion characteristics of droplets and infection risk caused by coughing.

The purpose of this study was to accurately predict the dynamics of SARS-CoV-2 infection–based on a 3D shell model and combining computational fluid and particle dynamics (CFPD) and host-cell dynamics–along the mucus layer of the upper airway. Assuming that a healthy person inhaled droplets coughed up by an infected individual via nasal inhalation, we focused on the infection dynamics of the virus deposited in the nasal cavity and nasopharynx. The distribution of the cough droplets deposited was preliminarily analyzed using CFPD. Using this distribution as an initial condition, we predicted the time series distribution for SARS-CoV-2 concentration in the mucus milieu depending on diffusion, mucociliary motion, and virus production. These results form an initial elucidation of an aspect of the virus’ transmission mechanism in the upper airway.

The ongoing health and economic crisis related to the coronavirus has led to the reimagining of office work with the widespread adoption of teleworking. Despite the socio-economic benefits, a comprehensive assessment of the environmental impacts of teleworking in Canada is currently needed. Using ten participants from the Greater Ottawa Region, a longitudinal pilot study was used to quantify energy usage and GHG emissions in three domains: home office, transportation, and information and communications technology, using HOBO sensors, meters, and existing sources. Working full-time from home commonly results in a net reduction in energy and GHG emissions compared to conventional working arrangements due to reduced daily commute, especially when the distance from home to their company office is long. A net increase results when there is an increase in non-work-related commute. These findings suggest that the impact of teleworking is highly dependent on household energy-related behaviors, habits, and purchasing decisions.

The observed patterns in energy consumption in buildings during the COVID-19 lockdowns offers a great opportunity to see how people might behave in the future as they are expected to stay more often at home due to telework. This work analyses the energy consumption of a 40-unit social housing building located in Quebec City, Canada. The consumption of electricity and hot water before and during the pandemic is compared to see if lockdowns induced changes in energy consumption patterns. The comparisons are based on statistical tests and are applied for the whole building. Results show that there was a slight increase in overall consumption during the most stringent months of lockdown, but that the more important change was that consumption occurred throughout the day instead of being concentrated during the evening, as observed before the pandemic.

Inhalation exposure to various types of airborne particles is an important risk factor for human health. This study predicted particle transport and deposition in a realistic human airway model during breathing and observed the effects of steady-state flow and transient flow on the deposition fraction using computational fluid dynamics (CFD). To evaluate the effect of the transient breathing profile on particle transport and deposition in the respiratory tract, we reproduced two unsteady breathing cycles with different breathing time scales. The particle dispersion analysis targeted approximately 50,000 or 75,000 particles, with aerodynamic diameters ranging from 2 nm to 10 µm, randomly placed near the nostril. Under transient breathing conditions, a total of approximately 50,000 or 75,000 particles were continuously released at each time step during the inhalation period. As a result, a significant difference in particle deposition and transport to the lower airway region was confirmed for different breathing patterns.

The world has been suffering from the COVID-19 pandemic since early 2020. As the high population density and complicated ventilation conditions in the indoor environment, deep investigation on indoor disease transmission was vital to prevent the spread of the virus. With the plenty studies on this novel Coronavirus, the exhaled droplet that contained infectious pathogens is identified as the major viral vector. Nevertheless, the transmission mechanisms with human thermal plume plumes, different human respiratory behaviour, and different ventilation conditions have not been evident. In this study, the virus transmission was simulated in a full-scale meeting room with ten manikins. Coughing was considered the respiratory icon behaviour and assessed in this study. Outcomes of this study demonstrated that the movement of infectious droplets could be significantly affected by the ventilation conditions, centralized ventilation scheme was found to have better performance to reduce infectious risks for residents in the indoor area.

While the teleworking concept as an energy-efficient alternative working arrangement has been around since the 1970s, the mandatory shift to working remotely during the COVID-19 pandemic created the possibility to investigate the impacts of teleworking in more detail. However, the literature is scarce in this area, and therefore, it is essential to investigate the impacts of teleworking on teleworkers’ energy-related behaviors. As a result, this study aims to explore the overall impacts of teleworkers’ choices and preferences that have implications for overall energy use. This study relies on data from 14 participants, who moved at least 20 km away from their original place of residence within two years of the beginning of COVID-19 and started teleworking. In-depth interviews with teleworkers were focused on their backgrounds, changes in work and domestic routines and behaviors, preferences during teleworking, perception of costs associated with teleworking, and other related changes related to teleworking. Thematic analysis of the results suggests most participants moved to bigger houses in less accessible neighborhoods which caused acquiring cars. Most of them also opted to have dedicated home offices. Although some participants preferred working 100% remotely, the majority of participants had a tendency toward a hybrid model of working with an inclination toward more teleworking days per week. Overall, the results show teleworkers’ work-related preferences and energy-related behaviors for working remotely depend on marital status and household income. Based on the results of this study, it can be concluded that the overall energy use of participants has increased because of longer commute time, increased utility bills, and upgraded internet speed. This study maps out the interconnections between different domains of teleworking and provides data that can inform question item selection or development for quantitative surveys with larger samples.

Aerosol transmission of SARS-CoV-2 is a significant path of spreading COVID-19. Using high-efficiency particulate air (HEPA) filters in air duct systems or portable air cleaners (PACs) can help to curb aerosol transmission. However, air filters accumulated with virus-laden aerosols can become viral load sources and biologically hazardous objects. This paper reviews the detachment of bioaerosols from air filters and the associated infection risk, and introduces a novel silver-ion antiviral air filter that can inactivate airborne viruses. The virucidal efficacy of this filter on SARS-CoV-2 was evaluated with virological Cytopathic Effect (CPE) assay in a Biosafety Level 3 (BSL-3) laboratory, which shows that the filter was able to inactivate over 99.9% of SARS-CoV-2 after 30-min contact. This test result, together with the review findings, underlines the importance of using air filters with antiviral properties to safeguard public health.

The spreading of a disease largely occurs in buildings or in collective transport. The microenvironment flow processes around people contribute to the cross-infection risk, for example when two persons are standing close to each other. The cross-infection risk is also dependent on the macro-environment e.g., distribution of velocity and turbulence in the room, particularly when the background airflow is strong. The surrounding temperature and the presence of a vertical temperature gradient can also modify airflows in the microenvironment and subsequently influence the cross-infection risk. This work is based on a fully mixed flow in a room without a vertical temperature gradient, considering different room temperatures. Exhalation flow and aerosol distribution are studied by smoke experiments and the cross-infection risk is expressed as concentration in the susceptible breathing zone normalized by the concentration in the room.

Due to the new COVID-19 pandemic, which has imposed restrictions on the movement and agglomeration of people, most of the active workers in the country now carry out their professional activities remotely. With this in mind, the objective of this article was to find situations that interfere in a harmful way in this new routine, so that it is possible to make projects through simulations that provide the well being of the worker and family members. The research method has a qualitative approach based on post-occupancy analysis and data collection will be carried out through online forms. Throughout the article, it was noticeable that the causes of greater interference in the “home office” were noise, temperature and equipment for remote work. So, it is noted that everyday habits are modified with the social isolation necessary to prevent COVID-19 and become the “new normal”.

An activity wherein the usage of masks and social distancing, to prevent the spread of COVID-19 may not be possible, is not possible is during social gatherings over a meal or a drink. Droplets generated during a casual conversation during a meal can lead to the transmission of COVID, due to which restaurants and bars can turn out to be vectors for its transmission. Therefore, it is necessary to investigate the dispersion of respiratory droplets in such an environment. To that end, we carry out numerical simulations of droplets dispersion in a typical Japanese restaurant. The role of ventilation and air-conditioning systems on dispersion will be discussed.

Computerized maintenance management systems for commercial buildings, and more specifically unsolicited maintenance requests from occupants, are an under-utilized source of data that can be used to improve occupant comfort, health and safety, or energy efficiency. This paper describes a methodology to leverage these data and extract the benefits. The findings include many insights from case study data, including a breakdown of the type of maintenance tasks requested. The impacts of COVID-19 on maintenance requests are also investigated to reveal new insights about technician taskloads and response times during low-occupancy periods. These results indicated that most, but not all maintenance types decreased proportionally to the building occupancy levels during telework, and indicated that relative to pre-COVID-19 data an increase in janitorial requests should be expected when greater occupancy levels return. Lastly, a strategy for evaluating the performance of maintenance teams based on their response time to maintenance requests is proposed.

The current infectious diseases (i.e., COVID 19) has a dramatic impact on human life globally and unprecedented impact on economic and social life. Natural ventilation is a potential energy efficient and low-cost approach to remove airborne viruses from indoors, especially for public buildings such as classrooms. However, the dependence on local conditions (window opening mode and outdoor wind speed) is a potential reason to the unpredictability of natural ventilation efficiency and possible higher infection risk. This study aims to optimize the window openings design (size and location), based on which the low-cost fans are further considered to enhance the natural ventilation efficiency and decrease the infection risk. We performed the simulations to model the pollutant concentration and infection risk under different scenarios of infection sources and different window opening modes with or without using fans. Results showed that the airflow distribution performance index (ADPI) was increased by 17% and infection risk was decreased by 27% through optimizing the window opening mode and combining the low-cost fans. This work can be helpful to promote the low-cost and effective measures of mitigating infection disease.

One of the deadliest pandemics in decades is COVID-19, when the world had shut down after the declaration of the World Health Organization. Like the rest of the world, schools in France closed on March 16th, 2020, and slowly reopened from May 11th, 2020. This paper aims to find the amount of energy usage influenced by the COVID-19 pandemics during first, second and post-second lockdown in terms of occupants’ presence. For this purpose, measured electricity data and actual electric bills of IUT de Nîmes were analysed. In 2019, this campus utilized 2000 MWh of energy cost around €150,000. The amount of energy usage declined by 9% and economized 16% compared to 2020. Most of these reductions came from the energy conserved during the first lockdown period. The heating energy supply was cut off completely after the school closure, while the electric energy remained on average of 36 MWh/month during unoccupied period. It is later can be explained by the presence of predominant electrical devices to maintain buildings and unplugged technical equipment which continued to leach out trace amounts of energy.

Mechanical mitigation strategies can effectively reduce the transmission of SARS-CoV-2. However, until recently, multi-zonal airborne transmission studies are still rare. This study aims at evaluating engineering mitigation strategies for five selected commercial buildings (medium office, large office, stand-alone retail, small hotel, secondary school). A multizone airflow and contaminant simulation software, CONTAM, was used for evaluations. The effectiveness of a variety of mitigation strategies was predicted and compared, including increasing outdoor air (OA) ventilation rates, using air-cleaning devices such as MERV filters, portable air-cleaners (PAC) with HEPA filters, germicidal ultraviolet (GUV) irradiation, layering with wearing masks. Results indicate distinct airflow and thus quanta transmission characteristics in buildings with different mechanical ventilation systems. For confined spaces with packaged terminal air conditioners (PTAC), in-duct air treatment could be insufficient, and room treatment devices are needed.

Several guidance recommendations on how to operate air-conditioned buildings have been provided to reduce risks of indoor COVID-19 transmission. These include increased outdoor air intake, before and after occupancy indoor air purging and enhanced air cleaning. Using a whole-building energy model to represent a typical large commercial office building in Singapore, we evaluate the energy impact of these recommendations. In univariate analyses, increasing outdoor air percentage beyond 30% is associated with increased energy consumption by 11% and raised prevalence of unmet cooling setpoint to 80%. Utilising MERV14 media filters, upper room UVGI and high clean air delivery rates (CADR) per power portable air cleaners increased modestly the energy use intensity (EUI) up to 6.3%. However, operating UVGI at the AHU and social distancing measures resulted in energy savings. Full factorial simulation resulted in a total of 576 recommendation combination options with 72 that have energy performance index higher than − 10% and prevalence of unmet cooling setpoint lower than 15%. Through multivariate analyses comparing all recommendations, the dominant variables that influence energy consumption and unmet cooling setpoint are social distancing and outdoor air percentage respectively. The most energy efficient combination options involve the recommendation of 30% outdoor air, AHU UVGI and MERV14 filters. The results from this full factorial simulation may provide different combination options for building managers or owners to select from.

Correctly predicting contaminant transport in indoor environments is crucial for improving interior layouts to reduce infection risks. Prediction based on measured airflow may overcome the challenges of measuring complex boundary conditions and inaccurate turbulence modelling. CFD simulated airflow, including the 3-D velocity components and turbulence kinetic energy, was used as surrogate for experimental measurement based on the spatial resolution of ultrasonic anemometers. A stand-alone Markov chain solver was developed for calculation. Two cases were used to assess the feasibility of the proposed method, and the calculation results were compared with the benchmark calculated by the commercial CFD software. It shows that, for simple airflow, like that in an isothermal ventilated chamber, the trend contaminant transport and peak concentrations can be predicted reasonably well. However, for complex airflow, like that in non-isothermal chamber with occupants, only the general trend of contaminant transport can be reasonably predicted.

The purpose of this study was to analyze the transport dynamics of inhaled gaseous odorants in the respiratory tract and investigate the characteristics of the adsorption flux on the olfactory epithelium tissue. Following the procedure of the perceptive air quality test described in ISO 16000–28, we analyzed the adsorption flux on the olfactory epithelium tissue under transient breathing conditions using a coupled computational fluid dynamics and physiologically based pharmacokinetic (CFD-PBPK) model. In the calculations, a comprehensive model that integrates human body geometry with realistic airway geometry was used. Through the analysis, we obtained the following results: (1) The inhaled odorant concentration varied with the unsteady breathing cycle. (2) The adsorption flux on the olfactory epithelium tissue was low compared to that in the other nasal regions. (3) The difference in the adsorption fraction in the olfactory epithelium region was confirmed between the orthonasal and retronasal pathways. Thus, the two different pathways might have a significant impact on the efficiency of odorant transportation to the olfactory epithelial region.

Electrospun nanofiber air filters can achieve excellent particle filtration efficiency and low pressure drop. Therefore, they can potentially be applied in buildings for controlling indoor particulate matter concentrations. To improve filtration performance, this study developed a design and fabrication approach for electrospun nanofiber air filters with minimized pressure drop and optimized structures. First, this research developed semi-empirical models for calculating the pressure drop and PM2.5 filtration efficiency of nylon electrospun nanofiber filters using the fabrication parameters of electrospinning time and nylon concentration. With the proposed models, this investigation then proposed an optimization approach to minimize the pressure drop of the nanofiber air filters. For a given air velocity and PM2.5 filtration efficiency, one can minimize the pressure drop by finding the optimal solution concentration, while the target PM2.5 filtration efficiency can be achieved through adjustment of the electrospinning time.

This study aims to experimentally investigate the efficiency of a mechanical ventilation (MV) system, with and without the presence of air cleaners (ACs), to reduce the aerosol particle concentration in indoor spaces. The measurements were performed in a case study room equipped with a MV system, an AC, and 16 artificial aerosol generators (AGs) to represent an intentional multifold of human respiratory aerosol generation. Five aerosol particle sizers (APS) were used to measure the concentration of the aerosol particles. Overall, five different scenarios (with and without ACs) were reproduced. The results show that when only AC was used 98% of the aerosol particles were removed within 30 min and the concentration peak was about half of that found for the scenario without AC and MV. When AC and MV with three different modes were used in combination, the concentration peak was found even less pronounced, with about 98% aerosol removed in about 20 min for 1st, 15 min for 2nd, and 13 min for 3rd ventilation settings.

Indoor air quality (IAQ) and health are major areas of concern in northern housing, and are influenced by ventilation. Northern housing experience varying occupancy (overcrowding) and indoor conditions (high indoor activities), leading to higher indoor pollutants and moisture that need to be controlled to avoid mold problems, deterioration of the building and the IAQ. To better address these issues ventilation needs to become smarter and designed to automatically adjust ventilation based on indoor needs. This paper present results from a side-by-side testing of a CO2-based demand-controlled dual core energy recovery system versus a conventional single core energy recovery ventilator (ERV) using twin houses with simulated occupancies. The results showed that the implemented strategy based on a CO2 sensor network connected with the dual core ERV unit was simple and efficient to adjust the ventilation for varying occupancy with improved control of the CO2 concentrations in the test house, ensuring daily mean concentrations below 1000 ppm in the main floor and master bedroom, and below 1200 ppm in bedrooms with closed doors. Compared to low simulated occupancy, power consumption of the CO2-based dual core ERV increased by 53% during higher simulated occupancy.

Conventional single core heat/energy recovery ventilators (HRVs/ERVs) installed in the North are plagued with problems and require defrost strategy or frost protection for operation. Frequent defrost cycles could lead to under-ventilated homes and frost protection by a pre-heating system (electric or hydronic) could undermine their efficiency and add to the energy cost. This paper presents results from an extended monitoring of a novel dual core energy recovery ventilation system of a triplex unit on the Canadian High Arctic Research Station (CHARS) campus in Cambridge Bay (Nunavut). The monitoring carried out over three heating seasons provided evidence about its long-term performance and resiliency, with proven continuous ventilation, withstanding outdoor temperatures below  − 40℃ without frost protection and achieving high sensible effectiveness around 50% in summer and up to 90% in winter. Results presented in this paper are limited to the period of June 2018 to May 2019.

Field measurements and building energy simulations (BES) are conducted in this study to analyze the influence of temperature set-point and ventilation rate on classroom indoor environmental quality (IEQ). This study aims to suggest the superior management strategy for balancing the learning performance and the energy consumption of air conditioning. We proposed a single-objective, as well as a multi-objective, optimization to clarify the optimal setting conditions of temperature set-point and ventilation rate. After considering the economic constraints, schools can derive the optimal combination of settings by referring to the Pareto frontier obtained from the multi-objective optimization proposed in this study.

The study and control of the airflow in the indoor environment is crucial. Computational fluid dynamics (CFD) allows detailed flow analysis, but faces challenges mainly in terms of computational efficiency. Modern data driven tools have lately received much attention for inexpensive airflow simulations. In this work we develop a coupled CFD–deep learning framework by employing a neural network to replace a classical zero-equation eddy viscosity turbulence model. A standard multi-layer perceptron is trained on TensorFlow and subsequently transferred and applied to a CFD solver in OpenFOAM. Training and testing were performed by collecting data from validated Reynolds-averaged Navier–Stokes (RANS) simulations of indoor airflow from literature. The new coupled framework enables accurate results with significantly faster prediction speed. A primary challenge remains in being able to train the neural network over a larger dataset and provide higher generalizability to the model with sufficient accuracy.

In China many old residential buildings use the direct discharge of cooking smoke into the corridor, the environmental and health problem of public areas of old residential buildings is gradually attracting more attentions. Based on the field measurements and evaluation of cooking smoke emission concentration in a typical five-storey old residence in Shanghai, the diffusion law of cooking smoke in residential public areas were obtained. It is found that during cooking activities, the cooking smoke pollution in the corridor was serious. Moreover, the cooking smoke from one layer mainly diffused to the upper layer in the corridor, and the concentration of cooking smoke increased with the increase of floor number. On this basis, three types of reconstruction strategies were put forward, including installing induced draft fans in corridor, exhausting cooking smoke from the roof with a shaft and discharging purified cooking smoke by purifier directly. This study helps to guide the old residence reconstruction with the direct discharge of cooking smoke into corridor.

Carbon dioxide (CO2) sensor grid configuration has implications on indoor air quality (IAQ) when used for demand-controlled ventilation (DCV). The non-homogeneity of occupancy across a floorplate can result in high localized concentrations of CO2 which may go unaddressed if unmonitored. This study identifies zone locations, occupancy-types, and occupancies that are more likely to result in high CO2 concentrations based on a simulated floor of an academic office building to help inform how CO2 monitoring on the floorplate should be prioritized for DCV and IAQ purposes. It was found that smaller multi-occupant zones disproportionately experienced high CO2 concentrations, while core zones and single-occupant offices with lower ventilation to seating capacity ratios also experienced elevated CO2 concentrations under a variety of occupancy scenarios. Future work will expand the simulated occupancy scenarios and simulate DCV with various CO2 sensor grid configurations to quantity the impact on IAQ and energy use.

Fibrous-particle (asbestos) exposure induces lung cancer, mesothelioma, and fibrosis. Man-made vitreous fibers (MMVFs) share some similarities, and so their related health effects need to be determined. Computational fluid dynamics (CFD) was used to simulate the airflow patterns (Eulerian method) and to predict the particle behaviors (Lagrangian method) in human upper airways. Results of the simulation showed high agreement with the experimental data. A higher deposition of spherical particles was observed at a specific aerodynamic diameter. The nasal vestibule and turbinate had the highest regional deposition fractions for both spherical and fibrous particles. Fibrous particles present a greater exposure health risk than spherical particles under similar breathing conditions and aerodynamic sizes.

Ventilation is one of the most important methods to control the transmission of airborne pathogens such as SARS-CoV-2 inside buildings. In many spaces such as hospitals, senior living centers, cleanrooms, etc., the desired cascading of airflow from one space to another is achieved by controlling the pressure and airflow in spaces. The ceiling plane can be a significant source of leak between the occupied space and the plenum above. Although other elements in the ceiling plane, such as lights, grills, sprinklers, pipes, etc., contribute to the overall leakage, ceiling tiles present the largest area for potential leakage in the ceiling plane. In this study, air leakage through a typical ceilings system and a better sealing, gasketed ceiling system, held by clips to the grid was measured using ASTM E283 test method. The leakage rate through typical ceilings is more than 4X that of gasketed ceiling at 0.015″ W.C. The benefits of reducing leakage at the ceiling plane by using a better sealing tile (gasketed) were assessed using CONTAMW in a Protective Environment Room (PER). A standardized pressurized patient room (ASHRAE Std. 170 ANSI/ASHRAE/ASHE Standard 170–2017 Ventilation of health care facilities), which consists of a patient room, anteroom, and a restroom, was employed. The leakage rates for the standard and the gasketed ceiling tile used in the model was 3.04 and 0.16 cfm/ft2 @0.016″ W.C., respectively. The pressure and flowrates generated by space pressurization were studied by predicting flowrates and pressures across each building element. The results show a reduction in the parasitic losses with the tighter ceiling tiles and better control on the leakage paths. The flows required to maintain pressurization are also reduced with the tighter ceiling. The airtightness of the ceiling tiles is also expected to reduce the net flow rates needed to achieve target pressure differences between a room with special pressurization requirements and adjacent spaces. This makes pressurization more energy efficient. Two practical examples of the use of gasketed ceiling tiles are illustrated. This study shows that providing a tight seal with gasketed-ceiling tile between the occupied space and the plenum can be an important part of an effective and economical solution to convert existing spaces into pressure-controlled spaces, or even improving the performance of existing pressure-controlled spaces.

One of the major concerns for fire and smoke experimental studies is providing the efficient but safe fire/smoke source. There are methods such as cold smoke and hot gas (hot smoke test), each of which has their specific limitations of cost and safety. This paper proposes an alternative method of helium smoke in simulating smoke spread involving BIPV façade. In this method, the full-scale model is scaled down by Froude modelling, and then helium smoke study is conducted to compare the result to small-scale real fire. Also, the small-scale real fire is compared to the full-scale real fire. And finally, to prove that the method works well, the small-scale helium test results are compared to the real scale fire study.

The displacement ventilation (DV) system with stable and continuous temperature stratification is conducive to energy conservation and indoor air quality improvement. However, the disturbance mechanism of human movement on temperature stratification in the DV system is less studied so far. In this paper, the concept of the internal wave is employed to explain the temperature variation during occupant movement in the DV system. The conclusion is that the moving human can produce two kinds of internal waves caused by air extrusion and air entrainment. The internal wave is anisotropic, and it can only transmit in the occupant moving direction. The extruded air and the entrained air hitting the wall can gain the internal collisional wave in the opposite direction of the moving human. The current study provides new insights into the disturbance of human movement on temperature stratification in the DV system.

Both forestry wastes and agricultural crop residue are good biomass resources. However, although there has been abundant research on the co-combustion of biomass and other types of feedstock, the co-combustion of forestry waste and crop residue has not been fully studied. In this study, five pellets made of wood sawdust (WS) and peanut shell (PS) were named as W10P0, W7P3, W5P5, W3P7, and W0P10 according to their own mixing ratios. The five pellets were used in the combustion experiment and NOx and CO emission performance are investigated. The results show that proper mixing ratios can reduce CO emissions, while the mixture (W7P3, W5P5, and W3P7) emits more NOx than a single material (W10P0 and W0P10). The research will help promote our understanding of the co-combustion of forestry waste and crop residue.

Understanding the relationship between indoor environmental comfort and occupant behaviour is beneficial to promote the development of energy saving of buildings and increase the occupant’s satisfaction towards their housing. This paper reviews 4336 literature in this field by bibliometrics. At the same time, it makes a systematic analysis of the research hotspots, research characteristics, development contexts and future trends within the theme. The result indicates that thermal comfort has been a long-standing research hotspot, but little attention has been paid to occupant behaviour. The early research into indoor environment was based on climate chamber experiments and the design of building equipment systems. Subsequently, researchers began to focus on the relationship between occupant behaviour management and indoor environment, and the research objects have been enriched. The multi-objective optimization and data mining of machine learning are at the frontier of research. The research value has shifted from building energy saving buildings to green buildings that are comfortable for human to live in.

This paper outlines a methodology for disaggregating building-level occupancy data into zone-level occupant counts using opportunistic data from Wi-Fi access points, motion detectors, and CO2 sensors via sensor fusion for a floor of an academic office building. The efficacy of different combinations of data for this purpose is explored and the occupant-count estimates from these different combinations of data are compared to one another. The impacts of different sensors, sensor grid densities, and their data on the occupant-count estimates are discussed. Historical CO2 data are analysed to determine if instances of under- or over-occupancy as estimated by this methodology are reflected in the measured CO2 trends where available. The results indicate that additional sensor data improves the disaggregation model’s ability to estimate which rooms are over-occupied, with the granularity of the Wi-Fi access point grid and inclusion of CO2 data causing the largest increase in purported accuracy.

A parametrical investigation has been carried out to explore the velocity and temperature behaviour of wall confluent jets (WCJ) when used to heat and ventilate a test room, which mimics a greenhouse. This study assessed how the outdoor air temperature and supply air temperature affect the velocity and temperature profiles of the WCJ. The study also evaluated how WCJ can be used to eliminate film-wise condensation on greenhouse enclosure surfaces. Constant current anemometers (CCA) and T-type thermocouples were used to measure air velocity and temperature of the WCJ, air and surface temperature in the cooling chamber and test room. This study found that the supply air temperature affects the magnitude of the WCJ’s temperature in each region but the pattern (shape) of the dimensionless temperature is unaffected. The study also showed that both magnitude and pattern of the dimensionless temperature profiles are unaffected by the outdoor air temperature in all regions of the WCJ. The dimensionless velocity profiles under isothermal and non-isothermal were similar, but the magnitude of the profiles increased as the supply air temperature increased in the merging and impinging regions of the WCJ. WCJ formed a boundary layer of warm fluid on the nearby wall; thus, can be used to reduce condensation on the inner surfaces of the greenhouse enclosure.

The velocity field of the large-scale circulations (LSC) in turbulent mixed convection is analysed by means of 2D2C particle image velocimetry (PIV). The experiments are carried out in a small-scale model room resampling a generic passenger cabin. To achieve wide ranges of dimensionless numbers, pressurized dry air is used in the SCALEX facility. Three different LSCs have been found, depending on the Archimedes number $$Ar$$ Ar .

Microbial volatile organic compounds (MVOCs) are produced during the metabolism of mould growth. The measurement of MVOCs is considered as a possible means to identify suspected or hidden mould growth, as well as indoor moisture problems. In this study, volatile organic compound (VOC) emissions were monitored from mould inoculated building materials (plywood and spruce) in order to identify MVOC profiles emitted during mould growth. Inoculated samples were incubated in a customized incubation chamber under constant environmental conditions, and monitored for VOC emissions and mould growth. Air samples were collected from the incubation chambers using Tenax TA tubes and analyzed by gas chromatography—mass spectrometry. Experimental results showed that the identified MVOCs were mostly ketones and terpenes/terpenoids, whose evolutions were correlated with mould growth activity, even prior to any visual signs of growth.

Air conditioning-related problems in the interior environment of cars are becoming more pronounced. In this study, we used a metagenomic method to analyze the fungal flora on the coils and filters of five car air conditioners in order to characterize potential microbial contamination of the cabin. Airborne fungal flora was also analyzed in the car with the engine running. The results of ITS2 analyses indicated that most of the detected fungi originated from plants and the general environment. Fungal growth was promoted by the air conditioner condensation water, as many of the identified species were hygroscopic and drought-resistant.

An emissions barrier was used in premises with indoor air complaints due to emissions from the buildings in question. The emissions comprised chlorophenols/chloroanisoles and polycyclic aromatic hydrocarbons (PAH) from treated wood, volatile organic compounds (VOC) from PVC flooring and/or the glue used to paste the flooring onto a concrete slab, and radon from radium-containing aerated concrete. Attaching the barrier at the surfaces from where the emissions were spread (floor, walls, ceiling) resulted in a fresh and odour-free indoor air and a substantial decrease of the emissions. We conclude that using an emissions barrier in buildings may be an efficient way of restoring a pleasant and healthy indoor air.

Correctly predicting indoor particle transport is crucial for the design of air distribution to reduce the transmission of airborne infectious diseases. In recent years, a model for predicting particle transport based on the Markov chain technique has emerged, with the advantage of fast computing speed. However, the turbulence diffusion modeling approach in this Markov chain model is semi-empirical without justification. Thus, the present study aims to increase the accuracy of Markov chain model in predicting particle transport by modifying the turbulence diffusion. Laboratory experiments on transient particle transport under unsteady periodic ventilation were conducted to validate the proposed model. The results show that the improved Markov chain model can correctly predict transient particle transport in comparison with the experimental data and was more accurate than the existing model in predicting transient particle transport.

Cross-ventilation is inadequate in double-loaded inexpensive flats, especially on the leeward side of the structures, widespread in tropical developing nations. Using validated CFD models, this work attempts to parametrically assess closed-vertical void designs to optimise the ventilation efficiency of a double-loaded apartment building. The aspect ratio of the void, pilotis size and fin size with varying wind directions are among the settings. The results revealed that the windward side was impacted more than the leeward side by wind direction, but the leeward side was influenced more by aspect ratio than the windward side. On the leeward side of the building, the decreased aspect ratios allowed for improved natural ventilation. The half-size fin generated an evenly distributed wind pressure difference on the windward and leeward units. These findings might help improve natural ventilation to develop sustainable design standards for including a closed-vertical void in mid-rise residential complexes in the tropics.

In this work, the role of Ag modification on acetone conversion using ceria catalysts under vacuum ultraviolet (VUV) illumination was explored. The XPS and Raman analyses were employed to characterize the structure of the as-synthesized Ag/CeO2 catalysts. The attained results showed that introduction of Ag increases the oxygen storage capability of ceria, favoring the formation of oxygen vacancies, and resulting to lattice oxygen activation in the CeO2 structure. Moreover, by addition of silver to ceria photocatalysts, the catalytic ozonation would be involved as an active oxidation mechanism for the removal of acetone. Therefore, by Ag modification of CeO2 catalysts, not only do the acetone conversion and O3 decomposition increase but also the selectivity of the reaction is improved.

The purpose of this study is to reproduce CO2 emission from a human in the chamber experiment and investigate a relationship between CO2 emission rate and indoor air environment. A “digital-twin” model of the experiments for estimation of CO2 emission rate was developed by CSP integrated with CFD technique. As a result, the inhaled CO2 level depended on the flow patterns around the occupants. Additionally, the reasonable reduction of tidal volume by low IAQ could explain the reduction of CO2 emission rate in the chamber experiment.

Convective heat transfer coefficient (h) between a building surface and adjacent air determines the heat loss (or heat gain) from thermal masses. Quantifying h is challenging, as h depends on many factors such as building geometry, surface orientation and flow velocity near the surface. This paper aims to quantify h for the indoor surfaces in the Y2E2 Building at Stanford University using computational fluid dynamics (CFD) simulations. Nine cases of large eddy simulations, covering a wide range of operating conditions, are run to derive the range of h for each surface under buoyancy-driven ventilation. Comparing h derived from CFD simulations to h calculated using the 2012 ASHRAE Handbook shows that their differences could exceed one order of magnitude. These significant differences suggest that general correlations of h may not be applicable to real buildings with complex geometry, and therefore h should be derived on a case-by-case basis for different buildings.

The importance of indoor air quality (IAQ) has been recognized for centuries, with increased interest in the relationship of IAQ to energy efficiency and other building performance issues in the last several decades. Managing IAQ requires an understanding of the complex interactions between buildings, systems, occupants and environmental conditions as well as the interactions of IAQ and related performance issues. Most efforts to address IAQ are specific to the affected buildings, spaces and occupants, and those tasked with solving IAQ problems generally need to consider a wide range of information that can be difficult to access and manage. Knowledge Representation and Reasoning (KRR) techniques are being developed for buildings to organize such information and ultimately enhance building performance. Specifically, Semantic Web technologies have been used to create models representing building system components and their relationships. Such models can improve building analytics and enterprise knowledge management to access the contextual information necessary for automated fault detection and diagnostics, control system configuration to optimize energy use, building commissioning and other applications. This paper describes how KRR can be used to address IAQ with discussion of an example using measured indoor CO2 concentrations to evaluate ventilation rates.

Sleep is essential to maintain health for the elderly. Few studies have investigated the effect of the bedroom environment on the sleep quality of the elderly. A field study was conducted in winter in Shanghai, China, aiming to measure the bedroom environment for the elderly and how this affects sleep quality. Forty-eight elderly aged at least 65 years old participated in this study for consecutive six nights. The bedroom air temperature, relative humidity and CO2 concentration were monitored continuously throughout the test period for each subject. The sleep quality was measured objectively (wrist-worn sleep tracker). The results show that the air temperature increased by 1 °C, the sleep efficiency increased by 0.86%, the percentage of deep sleep increased by 0.27% and percentage of wake decreased by 0.43%. When relative humidity increased by 1%, the sleep efficiency increased by 0.19% and the percentage of light sleep decreased by 0.09%. No significant effect of CO2 concentration on sleep quality was observed. The interactive effect of air temperature and relative humidity on sleep efficiency was significant. The results indicate that great efforts are needed to improve bedroom thermal environment in winter for the elderly.

Among air pollutants generated in cities, NO2 occurs in vehicles and seriously impacts passengers’ health waiting at bus stops. Therefore, installing an air purification system inside a bus stop was proposed to improve public health. This study confirmed the effect of removing NO2 using computational fluid dynamics when an air purification system was installed at a bus stop. The NO2 concentration inside the bus stop was observed according to bus positions under three scenarios. The scenario with the highest NO2 concentration at the bus stop was divided into three additional scenarios according to the installation location of the air purification system to confirm the NO2 removal effect. The NO2 concentration was the highest when the bus exhaust pipe was in the middle of the bus stop entrance, and when both an air purification system and air curtains were used, the NO2 removal effect was approximately 89.4% higher than the worst-scenario.

This study investigated aerosol transport inside a full-scale, 11-row, wide-body aircraft cabin mock-up, by quantifying an aerosol surrogate exposure. Cabin was supplied with fresh air through two supply air diffusers running along both cabin sides. Heated manikins simulated passenger thermal load. Tracer gas injected as an aerosol surrogate at breathing level at four different hypothetical contagious passenger locations was sampled radially up to 3.35 m from the source for 30 min per location. Twenty test pairs of different source and sampling locations were evaluated. The results confirmed source distance as the strongest exposure determinant. Average concentration at 3.35 m was 89% less than the source location. Concentrations were consistent in all directions at more distant locations but varied near the source due to cabin airflow patterns. In two tests, some concentrations further away from the source exceeded those near the source for a mid-cabin source, and a window seat source.

The COVID-19 pandemic highlights the importance of improving building indoor air quality to reduce occupants’ chances of contracting airborne illness. The ASHRAE Epidemic Task Force (ASHRAE-ETF) released several COVID-19 mitigation strategies at the onset of the pandemic. This study explores four of those recommendations for reducing transmission of COVID-19 inside buildings: (1) 100% outdoor air ventilation, (2) MERV-13 or better filters, (3) demand control ventilation removal, and (4) HVAC flushing mode. These recommendations were simulated and assessed using ComStock, a model of the U.S. commercial building stock. The study showed the 100% outdoor air ventilation recommendation had the largest impact on energy consumption, noncoincident peak demand, and thermostat violations. Removing demand control ventilation had the smallest national aggregate impact, installing MERV-13 filters led to slight increases in energy use and peak demand, and HVAC outdoor air flushing led to modest energy use and peak demand increases.

An integrated model that simultaneously couple CONTAM and WUFI has been developed to predict indoor air quality, moisture performance and thermal comfort. The coupling of CONTAM and WUFI has been performed using co-simulation method. In this method, the control variables of airflows are coupled as infiltration, natural and mechanical ventilation between equations of contaminant flow balance in CONTAM, the heat and moisture flow balance in WUFI. The accuracy of the integrated model is verified using the paired sample t-test method. The reason for the high accuracy of the integrated method, however, is that the airflow control variable in the present model is modified because of the co-simulation mechanism of CONTAM and WUFI. The values for effective leakage area and fan flow rate in CONTAM and values for infiltration and mechanical ventilation rate in WUFI are defined by the user as the input airflows data. To test the accuracy of the integrated model compared to single models, two scenarios of leaky-fan off, and leaky-fan on are defined for a three-story house in Montreal. Considerations are given for the indoor PM2.5 as indoor air quality, indoor relative humidity as moisture performance, predicted percentage of dissatisfied as thermal comfort measures. The results of the indoor air quality measure simulated by CONTAM, and the results of the moisture performance and thermal comfort measures simulated by WUFI are compared with those obtained by using the present model. The results showed that the optimal scenarios outputs simulated by the present model are different from those obtained using single models. The advantages of using the present integrated model are contained in the paper.

An experimental investigation is carried out to assess the particle collection efficiency of a non-woven activated fabric carbon (ACF). Measurement of pressure drop proved high permeability to the air (2.92 e−11 m2) compared to usual fibrous filtration media. The filtration efficiencies were determined for two poly-dispersed aerosols DEHS and iso-fine A2. The results showed an filtration efficiency higher than 70% for the particles of 1 µm. For the submicronic size range, ACF demonstrated superiority in capture of solid aerosols than liquid aerosols. This is likely due to the electrostatic forces which improve the filtration efficiency of the charged aerosols (iso-fine). The use of two layers of ACFs enhanced the filtration efficiency with DEHS by 20% for submicron sizes more than iso-fine where less improvement is observed. Finally, the non-woven ACF showed promising in micro-organisms capture since it proved 50% efficiency in the capture of MS2 virus.

The flow inside exhaust compartment of a full scale air handling unit is studied experimentally and numerically to characterize the flow non-uniformity, induced by the eccentric position of exhaust air inlet, and to evaluate their impact on the filters’ performances. It is about 2 bag filters installed side by side but not centered to the inlet. To predict flow structure, a 2D CFD model is developed using Ansys Fluent 14.5 and validated by pressure drop measurement and by comparison of fluorescein concentration injected upstream and collected on each filter. Results show that the volume flow rate is unequally distributed, in addition to significant flow disturbances and presence of backflow regions. This leads to staggered clogging of filter 1 reducing its permeability but not enough to stabilize the flow. Simulation with an extended domain including a centered inlet shows remarkable improvement of the flow distribution and pressure drop decrease.

This paper presents a prediction approach for indoor particulate matter (PM2.5, PM10) of two school gyms using a lumped model and an artificial neural network model. The aforementioned two models were developed based on the measurement data including indoor/outdoor PM2.5 & PM10 sensors, on/off status of energy recovery ventilators, and CCTV images of occupants. As a result, the artificial neural network and the lumped model had an accuracy within MBE 13.6% and −0.1% and CVRMSE 29.9%, 18%, respectively. It was found that indoor particulate matter was influenced by the outdoor particulate matter, indoor relative humidity, the number of occupants, and the degree of indoor activity. It is suggested that the model predictive control of the ventilators should be performed for better IAQ.

India is among the top five countries in the world for green buildings. However, data on actual energy performance and occupant satisfaction in these buildings are lacking. This paper seeks to apply a customised post-occupancy building performance evaluation approach for Indian green buildings (I-BPE), to evaluate the actual performance of two green-rated housing developments in India—representing the warm-humid and composite climatic zones. Both developments contained flats with and without air conditioning (AC). Results showed that although flats with AC had higher electricity use than those without, there was no significant difference between measured indoor temperature and relative humidity levels. Even the level of perceived comfort was no different across the two groups indicating a high level of adaptation of residents in non-AC flats. The methods and data gathered in the study can be used to inform the design of green housing development for different income groups in India.

This work presents an investigation of air ventilation systems to evaluate contaminant dispersion in the human-occupied spaces. The room model represents the rooms commonly found in apparel stores and shopping malls designated to try clothes. In this investigation, one occupant was assumed sick, and assigned to release contaminant gas from the mouth. Two ventilation systems (i.e. mixing and displacement ventilation systems) are incorporated in the room model in order to assess—by means of numerical steady-state simulation—the contaminants concentration in the room. The dispersion of contaminants in the room demonstrates that the mixing ventilation system is highly risky in terms of the spread of contagious airborne diseases.

As an alternative to Polysomnography (PSG), Fitbit Alta HR (Fitbit) could provide an easier way to monitor sleep. Since the accuracy of Fitbit sleep monitoring is unknown, this study was conducted to compare the sleep monitoring performance of Fitbit and PSG. Nine healthy young adults (5 males and 4 females; 24 ± 2 years old; Pittsburgh Sleep Quality Index < 5) were tested on three nights with different environmental conditions (different sleep measurements were detected by PSG). Paired T-test and Intraclass Correlation Coefficients (ICC) were performed for discrepancy and agreement analysis for sleep measurements. Results showed that the agreement between PSG and Fitbit was excellent for Total Recording Time (TRT, ICC = 0.901), moderate for Total Sleep Time (TST, ICC = 0.721) and Sleep Onset Latency (SOL, ICC = 0.622), close to moderate for Sleep Efficiency (SE, ICC = 0.443) and Wake after Sleep Onset (WASO, ICC = 0.466), poor for Number of Awakenings (NOA, ICC = 0.185), REM Sleep (ICC = 0.085), Light Sleep (ICC = 0.177) and Deep Sleep (ICC = 0.054). The present results indicated that Fitbit showed promise in measuring TRT, TST, SE, SOL and WASO, but had a poor performance in sleep staging for monitoring sleep of young adults.

We examined the effects of carbon dioxide (5000 and 1000 ppm) on responses to Influenza A infection. Mice were intranasally exposed to 5000 plaque forming units (PFU) of Influenza A and then to carbon dioxide (420 ppm room air, 1000, or 5000 ppm) for 6 h a day for 3 days. We show that virus given with 5000 ppm carbon dioxide (CO2) induced greater lung inflammation as shown by an increased migration of pro-inflammatory cells (neutrophils) into the lungs and other histological changes compared to virus and room air with 420 ppm CO2. Differences between 1000 and 420 ppm CO2 were not apparent. Exposure to CO2 at 5000 ppm showed no changes to the lung by itself. This work shows that CO2 below the 8 h-TWA was able to significantly increase lung inflammation in mice exposed to influenza A. This work highlights a need to evaluate ventilation and carbon dioxide with regards to health.

In the extreme cold of the Arctic, ERV effectiveness is reduced due to core frosting. This simulation study investigates the performance of an open loop air-based building integrated photovoltaic/thermal collector (BIPV/T) designed to preheat ERV supply air and to generate electricity. A finite difference model of the BIPV/T system was simulated, using local weather data and indoor fresh air requirements. BIPV/T design parameters such as tilt angle, and cavity depth were varied, with consideration of ease of construction for improved implementation in the Arctic. The BIPV/T helped increase the fresh air temperature supplied to the ERV over 16 °C and reduced defrosting time up to 7 h per day. The 40 m2 BIPV/T array produced up to 33 kWh/day of electricity and 7.5 kWh/day of thermal energy. The simulated BIPV/T energy generation was coupled with measured arctic residential energy usage and resulted in savings of approximately 30% in electricity and 10% in total net energy.

Natural ventilation is an essential element of sustainable buildings as well as a promising solution for mitigating indoor air quality (IAQ) issues such as those related to the spread of COVID-19. The current study analyzes wind-driven cross-ventilation efficacy for a low-rise residential building with complex geometry and internal partitions consistent with typical units. A high-resolution computational fluid dynamics (CFD) simulation is carried out using a 3D steady Reynolds-Averaged Navier–Stokes (RANS) approach with a realizable k-ε turbulence closure. Microclimate parameters include different wind speeds and wind directions for a configuration with openings on all four sides of the building. The results indicate that air changes per hour (ACH) increase with windward opening area, and the number of ACH has a linear relationship with the wind velocity. Furthermore, adequate natural ventilation requires rooms to have a minimum of two openings to influence airflow and enhance IAQ.

During the SARS-CoV-2 pandemic, the need to optimize ventilation in the hospital environment to lower the cross-infection risk of health-care workers has become evident. Local ventilation has been employed to enhance mechanical ventilation in in-patient recovery rooms. The present study applies computational fluid dynamics to analyse existing indoor air distribution of a hospital room as well as the respiratory droplets generated by a coughing patient. A local ventilation system in the form of a dental vacuum equipment was integrated and its location and design were considered. Additionally to this vacuum equipment, a push–pull system was also considered to compare performance through decrease in particle count in the breathing zone. Results show that although the alternative techniques reduce particles in the breathing zone, performance variation is greatly dependent on location. A push–pull system, which covers a wider area, is recommended.

In recent decades, urban design has faced two critical challenges: the impact of climate change and urban growth. Urban structures, mainly in the centre of metropolises, absorb heat during the day and create urban heat islands. Creating outdoor thermal comfort is a practical approach to mitigate this phenomenon. This research aims to assess the impact of changes in urban geometry on outdoor thermal comfort in a street canyon located in Mashhad. The outcomes suggest that increasing the H/R ratio may lower the ambient temperature. The study results reveal that modifications in this ratio might induce heat emission. Ratio change also affects the airflow and wind shadow phenomenon, which helps decrease the ambient temperature and adverse effects of urban heat islands. Increasing the H/W ratio without addressing wind flow may reduce outdoor thermal comfort in street canyons.

The evidence of airborne transmission of Covid-19 through respiratory aerosols, and the experienced restrictions on commercial activity reinforce the necessity of a paradigm shift in the design of building ventilation systems for this new post-pandemic context. Therefore, this study has used a coupled multizone-CFD code developed by the National Institute of Standards and Technology (NIST) and an infection risk model on ventilation analysis for a small office application. The influence of different ventilation modes and air filtration efficiencies on infection risk was assessed. The code was validated by a high-quality benchmark, and the results demonstrate that the simulations reproduce the basic performance of the ventilation strategies that were selected. The purposes of this study are to assess the reduction of airborne infection risk of Covid-19 due to ventilation strategies and to present a framework that may contribute to the selection of rational solutions for the future challenges in this new post-pandemic era.

Airborne transmission has been considered as one of the main infection routes for COVID19 [(Wang et al., Science 373, 981 (2021)]. To estimate the infection risk, the current framework is based on a fluid solver considering the influence of the temperature field, which simulates the indoor airflow affecting airborne transmission, and a Lagrangian droplet model to describe the path of the viral particle moving under the indoor airflow. For the fluid solver, fully compressible Navier–Stokes is adopted to accurately capture the thermal-fluid interactions with significant heat transfer such as the airflow near the LED lights. Additionally, the immersed boundary developed by authors is used to construct the objects and assign the boundary condition in indoor environments such as the ventilation system, the human body heat and, the oral airflow. The fluid solver is validated by comparing the result of the mean age of air with the experimental result. With the information of ambient air’s velocity, humidity, and temperature obtained from the fluid solver, the path of the viral particle moving can be predicted using the Lagrangian droplet model considering the effect of the drag, natural convection, and evaporation. The droplet model is coupled with the Eulerian fluid solver with a weak-two-way coupling. Finally, the infection risk is quantitatively estimated by using Wells-Riley Model. The whole framework is based on a hierarchical structure named CUBE to perform the calculation in supercomputer Fugaku. With the framework developed in this work, the sputum droplet dynamics interacted with the indoor airflow to influence the infection risk can be qualitatively and quantitatively estimated.

The operating conditions inside the air handling unit (AHU) affect the performance of in-duct accessories, such as air purification devices. Proper design and sizing of in-duct accessories to meet performance targets require hour-by-hour investigation of the operating conditions of the AHU. However, there are technical barriers to designers of in-duct accessories to conduct such investigation. This paper presents a data mining approach with k-means clustering to create surrogate models to represent the hourly data. Hourly operating conditions of AHU for small office building in two different climate zones are clustered into multiple groups. The approach empowers AHU accessories designers to design and size their devices properly without conducting simulation.

The transmission of respiratory infectious airborne viruses such as COVID-19, and other influenza viruses inside buildings is becoming a vital area of study. The droplets produced from an infected person can spread to a large area inside an enclosed environment. In addition, a single cough or sneeze includes hundreds of droplets of different sizes that vary from 1 to 1000 µm. Consequently, those droplets, which are produced at speeds of up to 100 m/s, can reach and affect many people inside this environment. Their spread depends on many factors, including mechanical ventilation systems and natural ventilation. Most of the research examined the effects of mechanical systems on transmission. However, in educational buildings in developing countries where mechanical ventilation systems are unavailable, natural ventilation and infiltration become the leading causes of droplet movement. This work focuses on the transmission of COVID-19 droplets inside a classroom during the winter months when the windows are closed, and infiltration is the leading cause of movement. We study how air infiltration from cracks or gaps around exterior windows affects the droplets’ behaviour from an infected patient inside the classroom space—studying their reach and concentration. This numerical study uses computational fluid dynamic simulation (CFD) and stochastic and LaGrange equations. The results showed that the transmission of COVID-19 droplets only due to infiltration from around windows could be extended to wide ranges, reaching more students in the class at significant concentrations. The findings underscore the importance of considering the spreading induced by unintended air movement in enclosed spaces.

Before the COVID-19 pandemic, buildings accounted for more than 40% of the total energy. The need for improving indoor air quality (IAQ) not only during the pandemic but also in the post-pandemic era presents a unique opportunity for innovation that meets the needs in both environmental health and building energy efficiency. Carbon-dioxide concentration has been widely used for modeling and quantifying IAQ. Model predictive control (MPC) has been demonstrated promising potential in improving the energy performance of building heat ventilating and air conditioning (HVAC) systems. However, little research fell on both IAQ and energy efficiency improvement. In the paper, we developed a data-driven, coupled—IAQ and energy simulation method that combines thermal comfort and indoor air quality, specifically CO2. A new MPC algorithm was developed correspondingly to improve both energy-saving and indoor health. The results of a one-week, national-wide cooling season simulation indicate at most 50% energy savings while maintaining less than 700 PPM indoor CO2.

Membrane-based energy recovery ventilators (M-ERV) are becoming increasingly popular due to their significance in reducing the energy demand of heating, ventilation and air-conditioning (HVAC) systems. They reduce the energy consumption by energy exchange between the building exhaust and supply airstreams through an intermediate membrane. Various contaminants from the exhaust air may be recirculated to the supply air via the membrane of M-ERV. This can adversely affect the building’s indoor air quality. Compared to the energy performance, studies focusing on contaminant transfer (CT) in M-ERV are minimal. Membrane and contaminant properties and their interactions could significantly influence the CT in M-ERV. This study explains the effect of these properties and their interactions in detail. It could guide the selection of appropriate membranes to minimise the transfer of specific contaminants in indoor settings. It also provides guidelines for conducting future experimental studies on CT in M-ERV.

Ventilation systems have a high impact on air quality and thermal comfort. So, it is vital to improving the ventilation efficiency to parallelly reach a more uniform age of air (AoA) distribution and to decrease the total energy usage (less required outdoor airflow). In this paper, a validated numerical model has been used to investigate the effects of the perforation pattern and direction of air diffusers on the COVID-19 exposure time. Four types of perforated duct diffusers (PDD) were simulated using a k-ε Realizable turbulence model at the heating mode. Given the ASHRAE standards procedures and local AoA investigations, it is shown that vertical perforations would be more effective than the other types in terms of lowering exposure time. Besides, the results prove PDDs create a unidirectional pattern indicating their high preventing performances, in general.

The oxygen (O2) content in the supply air has a significant effect on stove pollutant emission performances, which has been rarely explored. In this study, we designed five O2 content of the primary air in a forced-draft biomass stove, which involved 17, 18, 19, 20, and 21%. We measured the emissions of carbon monoxide (CO), and particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5). The results showed that the CO emission rates slowly rose during the flaming period, and then sharply increased to the peak and finally declined to the low level during the extinguishing period. The PM2.5 emission rates increased during the flaming period, and decreased to the ambient level during the extinguishing period. The CO emission amounts had a U-shaped relationship with supply O2 content, but PM2.5 emission amount showed weak correlation with supply O2 content. This study provides useful information for clean stove design and improvement.

In the present study, a powerful double-intake and rotor squirrel cage fan is designed and optimized by using a developed optimization process loop based only on open source libraries: Dakota, Salome and OpenFoam. Eleven design parameters are selected in the impellers, blades and volute regions as optimization parameters to maximize the total efficiency. A coupling is achieved between CFD, Latin Hypercube Sampling (LHS), Kriging metamodel and the Efficient Global Optimization to find the optimal design. The Kriging and the EGO models predict accurately the efficiency ε with a maximum error of 1.22%.

The effects of solar radiation play an important role on human thermal comfort, especially within the near-window zones. When incorporating the solar effect into the thermal comfort model, the comprehensive solar-optical characteristics of windows have to be taken into account, especially when it came to a largely variant or unbalanced spectral distribution of a building window. In this work, we examined the thermal effects varying with different spectral characteristics of glazing systems and also preliminarily proposed a new indicator “thermal effect index (TEI)” that can be used to estimate the impact levels of window systems on indoor users’ thermal comfort in near-window zones. TEI could be used as a benchmark for assessing a window system’s potential impacts on indoor users’ thermal comfort, especially when direct sunlight is enabled in a space.

Given the global attention focused on indoor bioaerosols since the beginning of the COVID-19 pandemic, investigating the best ways to assess human exposure to airborne viruses, bacteria, and fungi is crucial. Humans, pets, plants, plumbing systems, HVAC systems, mold, dust resuspension, and the outdoor environment are among numerous sources of bioaerosols within indoor air. Diverse microbial communities populate human-occupied buildings. When looking for infectious agents or characterizing airborne microbial agents, several options are available, each of which forms the global picture of the bioaerosol complex nature. Following characterization of bioaerosols content using multiple approaches, including DNA sequencing, PCR, or culture, it remains a challenge to understand the role of each of the various components of bioaerosols and synergistic effects of multiple exposures in the development of diseases. Bioaerosol exposure can have varying effects on exposed individuals, and individual components of the complex mixture may have different effects, which are almost impossible to elucidate. This presentation will overview the main steps in bioaerosol investigation study and shed light on the limitations and strengths of various exposure assessment and experimental approaches and how collaborative research is necessary to elucidate disease transmission and data interpretation.

The purpose of this study is to investigate the combined impact of mask-wearing on cognitive performance and risk-taking behaviors. Participants were divided into a control group (N = 24) without and an experimental group (N = 27) with a surgical mask. Both groups completed the tasks in a warm environment (30 °C) where the conditions can reduce cognition and decision-making as well. These conditions are common in indoor spaces without sufficient air conditioning during a heat wave. Cognition and risk-taking behaviors were assessed using computerized tests. Results showed that mask-wearing in warm environment did not negatively impact cognitive performance, nor did it increase risk-taking behavior as the concept of risk compensation predicts, even when the CO2 concentration was elevated to approximately 29,000 ppm on average inside the mask. On the contrary, mask-wearing participants showed less risk-taking behaviors, slightly better response inhibition and better short-term memory. These results do not support previous findings suggesting that even a moderately increased indoor CO2 level can reduce cognition. We hypothesize that human adaptation effects (due to mask-wearing on a daily basis) make people less vulnerable to the adverse environment (i.e., excessive air temperature and CO2 levels), which will be investigated in the future studies.

The importance of air quality in residential buildings was well known, but indoor air pollution in office and school buildings has not received attention. This paper aims to analyse the exposure levels and health effects of indoor pollutants in office and school buildings in the Yangtze River Delta, China. The literature from 1980 to 2020 about indoor pollutants in this zone were systematically reviewed. The Composite Index Method was used to evaluate the indoor air quality. The DALYs (disability-adjusted life years) approach and the health risk assessment model were used to perform health risk assessment. The results showed that five pollutants exceeded the Chinese indoor air quality standard by more than 50%. In office buildings, 57, 59.7, 61.5, 61.1 and 85.7% of the samples had the higher concentrations of PM2.5, formaldehyde, benzene, TVOC and ammonia respectively, above the limits in the standards. While 55.2 and 100% of the samples had the concentration of formaldehyde and ammonia exceeding the standard in school buildings. Indoor air quality evaluation results showed that office and school buildings in the Yangtze River Delta were at the light pollution level. In addition, according to DALYs value, the health effects of various pollutants were ranked as PM2.5 > formaldehyde > ammonia > benzene > toluene > xylene. The indoor air pollution in the offices and school buildings in Yangtze River Delta, China had a high standard-exceeding rate. PM2.5 and formaldehyde played the important roles impacting the human health. The polluted levels varied among cities, and the health effects caused by different air pollutants were different.

To evaluate the effect of temperature on indoor air quality, an intervention experiment was conducted in a classroom of a university in Beijing. The indoor temperature in the control group was 27 ± 0.7 °C, while the indoor temperature in the intervention group was reduced to 24 ± 0.3 °C by running two distributed air conditioners. During both measurements, doors and windows of the experimental classroom remained closed, and air temperature, carbon dioxide (CO2) concentration and TVOC concentration in the classroom were measured. The air quality in the classroom was evaluated by 22 occupants staying inside the classroom and 22 visitors upon entering, separately. The results showed that compared with 27 °C condition, the CO2 emission rates by occupants decreased by 3.4% when the indoor temperature was reduced to 24 °C. Both occupants and visitors indicated that at 24 °C, perceived odor intensity was significantly reduced, and the satisfaction and acceptability of air quality were improved considerably.

This study addressed the effect of stable mechanical airflow generated by floor fans on thermal comfort zone in hot-humid conditions. 36 college students were tested in two adjacent offices with identical dimensions and layout. In hot-humid environments (26, 29 and 32 °C, with relative humidity of 40–60% and 70–90%), the subjective perceptions were investigated with air speed from 0.05 to 2.0 m/s. Subjects reached neutrality at 26 °C without airflow, at 29 °C/55% with air speed of 0.4 m/s, at 29 °C/85% with 1.2 m/s, and at 32 °C/55% with 2.0 m/s. At 32 °C/85%, thermal sensation remained warm even at a high air speed of 2.0 m/s. As air speed elevated from 0.05 to 1.6 m/s, the neutral temperature increased from 26.3 to 29.7 °C with humidity of 40–60%, from 26 to 27.7 °C with 70–90%. Accordingly, the upper temperature limit for 90% acceptability extended from 27.7 °C to 31.3 °C and from 26.9 °C to 28.6 °C, respectively.

By the end of 2021, over 273 million people have been infected with SARS-CoV-2. Many nonpharmaceutical interventions (NPI) have been commonly used to control the COVID-19. However, if such interventions were implemented during the stable period of the epidemic, it would waste unnecessary energy. Therefore, it is crucial to find a strategy on COVID-19 prevention and control according to the balance between infection risk and energy consumption. Long-range airborne transmission is one of the most important routes the spread of COVID-19. Considering the personnel metabolism and dynamic quanta generation in four indoor environments (home, office, classroom, subway), we established the infection risk-energy consumption model. After optimization, compared with the intervention of maximizing the fresh air volume in each indoor environment, energy consumption of homes, offices, classrooms and subways were reduced by 44.7%, 25.9%, 28.7% and 14.7% respectively in summer and 47.8%, 40.7%, 42. 1% and 21.2% respectively in winter.

In order to avoid the spread of pollutants in buildings, it is crucial to find the source of pollutants in time. Previous studies on identifying the pollutant source mainly focused on the spread of pollutants in pure indoor or outdoor environments. However, the dispersion of the pollutants between units can also cause an extensive range of risks. This study applied the inverse modeling of source identification in coupling indoor and outdoor environment, and the source location of pollutant transmitted between units was located. The inverse modeling of the adjoint probability method was adopted. A model with three-storey building of wind-driven single-sided ventilation was built. The results showed the accurate localization of pollutant sources transmitted in a single-sided natural ventilation building. This study intends to provide vital information for preventing the spread of pollutants or viruses in the building environment.

With the improvement of people’s requirements for living environment, the impact of indoor radon on health has also received extensive attention. During the literature screening process, it was found that most of the studies focused on residential buildings, while indoor testing of schools and office buildings was lacking. Therefore, a university in Shanghai was selected as the research object of this experiment, and the distribution of radon concentration in the university was described in more detail. In this paper, taking a university in Shanghai as an example, the student dormitories, office buildings, and classrooms of the campus were selected for indoor testing. The average concentrations were 16.18 Bq/m3, 22.67 Bq/m3, and 12.55 Bq/m3, respectively. Due to different ventilation conditions, different floors, and climate Conditions also affect radon concentrations, indoor radon is positively correlated with indoor temperature, relative humidity, and CO2.

Existing researches shown that the ultraviolet light at wavelength of 254 nm could inactivate the COVID-19. The parabolic reflector, a key component of the upper-room ultraviolet germicidal irradiation (UVGI) device, plays a vital role in the collimation and restraint of ultraviolet light. This study aims to simulate the collimation effect of four types of parabolic reflectors. They were divided into two categories to compare and analyze the uniformity of irradiance according to the exiting widths and overall heights. The results show that better parallel light can be obtained by placing the lamp at the focal point of the reflector. Increasing the overall height of the reflector can improve the distribution of the irradiation. The improved compound parabolic reflector (d) has higher efficient average value of light constraint and irradiance spatial distribution compared to the other types of reflectors.

While exposed to wildfire smoke, occupants are suggested to keep windows closed, according to US Environmental Protection Agency (EPA). Nevertheless, it is not always rational to follow the guidance when occupants’ risk perception is complicated by other contextual situations, such as when natural ventilation air is the only option for cooling. In this study, we analyzed the impact of window-opening behaviors on indoor PM2.5 concentration in a small office building located in Duchesne, Utah, during a wildfire in 2020. Two behaviors of the window operation were considered in the CONTAM simulation, (1) windows always closed and (2) windows operated based on a behavior model in the literature. The results show that the daily average indoor/outdoor (I/O) PM2.5 ratio during weekdays (8 AM–6 PM) was 0.12 ± 0.03 (Mean ± Std) and 0.21 ± 0.06 for windows always closed and windows operated with the behavior model, respectively. The corresponding indoor PM2.5 exposure level was 1.01 ± 0.58 µg/m3 and 1.76 ± 1.02 µg/m3 for the two conditions, respectively. The findings suggest the significance of occupant behaviors in assessing indoor air quality and exposure during wildfires.

The goal of this paper is the detection and diagnosis of dependent multiple faults (MDFDD) in an air handling unit (AHU) of an institutional building. Three machine learning models are developed using experimental data, the artificial neural network, decision tree, and random forest. The models predict the air temperature that should be measured during normal operation conditions in AHU. Artificial faults are generated in this paper. If the residual of measurement and predicted value by ML models exceeds the threshold, a fault symptom is detected. The threshold is calculated by using the total sensor uncertainty and average root-mean-squared-error of the prediction using ML models over the training data set. With the aggregation of these two parameters the threshold is defined for the target sensor. Once the fault symptoms are detected, some rule-based models are used for the diagnosis. The results revealed good performance of the proposed model on the MDFDD.

Indoor fabric surfaces (e.g., clothes, curtains, and bedding sheets) can adsorb volatile organic compounds (VOCs) in wildfire smoke penetrating indoors. Meanwhile, the VOCs can be desorbed from the surfaces and re-emitted to air and linger indoors longer than a wildfire event. This preliminary study analysed the composition of VOCs in the oak-burning smoke and then measured the concentrations of six common VOCs (Guaiacol, 4-ethyl-2-methoxyphenol, 4-methyl-1,2-Benzenediol, 2,6-dimethoxyphenol, Vanillin, and 2-methoxy-4-propylphenol) adsorbed on cotton fabric for seven different exposure durations (0.16, 0.5, 1, 2, 4, 8, and 12 h). Smoke was generated by burning chips of oak, a common tree in California. The chemical analysis was conducted with gas chromatography-mass spectrometry (GCMS). We identified 46 organic compounds in the smoke from the fabric extractions. The results show that VOC accumulation on the cotton fabric increased with exposure time within 12 h and varies from chemical to chemical. The measured adsorption concentration reached 48.71 µg/kg (mass of VOC/mass of fabric) for 4-methyl-1,2-Benzenediol and 3.59 µg/kg for 4-ethyl-2-methoxyphenol corresponding to the highest and the lowest after a 12 h exposure.

The notion of thermal comfort has evolved through time, being the answer to various changes and influences in the built environment. Despite the common engineering definition, with measured ideal conditions matching with a state of neutrality, the concept is far more complex and requires a holistic multidisciplinary approach. Two main aspects are pivotal in the concept of comfort: expectation and adaptation, which depend themselves on several diversity-driving factors. However, their influence is nowadays generally neglected in buildings’ design and operation, and standards. The aim of this work is to understand and acknowledge, by means of a literature analysis, the different aspects that contribute to the inner workings of thermal comfort expectations and adaptation. The paper addresses all the different dynamics constituting the users’ thermal background and their attitudes towards the indoor environment. Only an in deep understanding of the possible differences between different users’ expectation and adaptation mechanisms will trigger an informed human-centric design process.

Solar radiation influences indoor thermal comfort then directly affects the occupants’ productivity and health. To clarify the impact of solar radiation, this study applied a comprehensive mean radiant temperature (MRT) algorithms which considered the different spatial condition instead of using the central point temperature to represent the whole space. Consequently, we summarized the impact of solar radiation on the long-term indoor thermal comfort evaluation in office building at different geographic location, and confirmed the discrepancy of thermal comfort evaluation results based on the MRT algorithms of unirradiated and irradiated by solar radiation.

Hypoxic environment drastically affects the health and well-being of sojourners at high altitudes. Current medical researches are not possible to meet the comfort needs of sojourners who suffer mild hypoxia. In this study, a method for evaluating the oxygenic comfort level of short-term sojourners at high altitude was established. Oxygen enrichment experiments were conducted, the subjective oxygenic response was assessed using a self-developed questionnaire, and physiological parameters were assessed. The evaluation indicators were screened by statistical methods. Hypoxic responses could accurately predict the oxygenic sensation by the artificial neural network. The area under receiver operating characteristic curve (ROC) of different oxygenic sensations was 0.630 ~ 0.913, and the prediction accuracy was 93%. A fuzzy mathematics method was used to evaluate the oxygenic comfort level. The weighted kappa coefficient was 0.825. According to the results of oxygen adaptation, oxygen enrichment at high altitude can reduce adverse hypoxia reactions and promote hypoxia adaptation.

A field study was conducted in a ‘ZEB ready’ office building using the radiant ceiling panel and open-loop ground source heat pump system in the cold region. The indoor thermal environment was evaluated based on the objective physical measurement and subjective questionnaire survey. Moreover, the energy consumption was calculated based on the record from the building energy management system. The system performances of intermittent and continuous operation methods were also compared in winter. The results show that the systems can achieve the required thermal comfort during the heating and cooling period, the indoor temperature is within 23–25 °C. More than 70% of staff declared ‘neutral’ in thermal sensation votes, which means most people were satisfied with the indoor thermal condition. Continuous operation leads to 19 kWh electric energy consumption per day more than intermittent operation, whereas the COP of the heat pump increases from 3.86 to 4.18.

The Predicted Mean Vote (PMV) model developed by Fanger predicts thermal sensation. It is used in the ISO-7730, while the ASHRAE-55 uses a modified version (PMVCE) to better account for cooling provided by air movement. We compared the PMV and PMVCE results against 64,825 thermal sensation votes collected in buildings. We found that for V > 0.1 m/s the PMVCE is significantly more accurate (48%) than the PMV (42%) in predicting thermal neutrality. In 95% of the cases, the error was less than one point. The overall PMV and PMVCE models accuracy were 35% and 36% for |PMV| ≤ 2 and 43% and 44% for |PMV| ≤ 1, respectively. Neither PMV model could accurately predict ‘cold’, ‘cool’, ‘warm’, or ‘hot’ thermal sensations. Consequently, the PMV should only be used in conditions close to neutrality within the range |PMV| ≤ 1. We open-sourced the source code. More research is needed to determine what is the cause of the prediction errors and if other PMV formulations are more accurate.

Radiant systems have the potential to save energy and thermal comfort. Currently, many researchers have studied the effect of thermal comfort in radiant asymmetrical environments. However, there are few focuses on the thermal comfort of the elderly. This study aims to investigate the effect of radiant asymmetry caused by the positions and radiant temperature of the radiant panel on elderly’s thermal comfort. The view factor between the radiant panel and the human body was determined using a ray-tracing method and combined with the physiological model to assess the thermal comfort of the human body. The findings revealed that ceiling radiation provided the best thermal comfort. The radiant panel’s temperature affects the skin temperature, standard effective temperature (SET), and thermal sensation vote (TSV). The elderly have a higher core temperature (0.22 °C) and lower skin temperature (0.26 °C) compared with the younger. This work can guide the radiant systems operation and terminal design.

This study tested a simulation workflow and evaluated design options for urban scale infill developments using environmental simulation tools. Options were evaluated for pedestrian thermal comfort in several design options for a tower in the park development in Toronto, Canada. Using Ladybug, ENVI-met, and Dragonfly, this simulation based-study compared existing, proposed, and optimized development options optimized for outdoor thermal comfortability. An agent-based simulation in Houdini was also used to consider how people might actually react and move in response to different zones of comfortability on the site. The study found that the preferred building massing on this site is either a podium high-rise or a mid-rise building with the highest density placed around the perimeter of a site. Testing both simulation workflows found that the Ladybug option is quicker and easier to use, making it more suitable for early stage design evaluation rather than ENVI-met. Future work should incorporate agent-based simulation in more detail, as it is only briefly explored as part of the workflows in this short paper.

Rhythm changes in physiological parameters of human thermal comfort have been the focus of heating ventilation and air conditioning (HVAC) control strategy research. This study analyzed the effect of human circadian rhythms on their thermal comfort and other subjective feelings during awake period by experimental methodology. Twelve healthy young students (6 males and 6 females) were recruited for experiments. Experimental data was obtained through experiments carried out at three constant ambient temperatures (18, 21, and 24 °C) in a microclimate chamber. The results show that the circadian rhythm has a significant effect on human’s thermal sensation. Predicted mean vote (PMV) in a cold environment will vary with time. Skin temperature and heart rate variability are suitable for observing the effect of circadian rhythm. Totally, the indoor thermal comfort can be effectively improved by changing the air-conditioning control strategy based on human rhythmic thermal sensation.

In this study, a comparison is made of overheating in multiple field-monitored buildings using several different overheating indices, including dry-bulb temperature, the heat index (HI), humidex (H), standard effective temperature (SET), wet-bulb globe temperature (WBGT), discomfort index (DI), and summer simmer index (SSI). The field monitoring was conducted in the city of Montreal, Canada over the summer of 2020 at six school buildings and two hospital buildings. In at least two typical rooms of each building, temperature and humidity sensors were installed; a total of 34 rooms were instrumented in these 8 buildings, and the rooms are facing different orientations and are located on different floors. The extent of concordance amongst the different overheating metrics was examined by correlation analysis. The result from this study provides an evaluation of the similarity between the different assessment metrics and helps identify the assessment approach that is the most representative of the methods evaluated.

Electrified vehicles are becoming increasingly prevalent in our lives, and they will take over in the next years. Given the differences between electrified and combustion engine vehicles, a new strategy is required to the problem of maintaining adequate thermal comfort within the vehicle while reducing energy usage. This study presents one phase in the creation of a “personal comfort system” for electric vehicles, which allows for local heating or cooling of electrified vehicle passengers while reducing electric energy usage. Cold weather poses two major issues for electrified vehicles: cold air reduces battery efficiency and activating the heating system drains the battery. We present a smart technology that allows local heating or cooling of electrified vehicle passengers while reducing electric energy usage. As previously demonstrated in the literature, such systems would ideally target local body surfaces with high temperature sensitivities. To that purpose, we created a system for determining local thermal sensations and sensitivities for skin spots scattered throughout regions of interest for a personal comfort system embedded in passenger seats. In this paper, we discuss the process of creating distribution maps for those regions of interest, using high density observations from over 150 measurement points. The neutral base temperature was 32 ℃, while the spot stimulation temperature was + 8 ℃.

Improving comfort is important for people’s health and effectiveness. In traditional approaches, improving thermal comfort may increase building energy consumption. Passive building design has the potential to reduce energy consumption. Therefore, this paper proposed the green glass space (GGS), a passive design that connected the transition space between outdoors and indoors. Based on the thermal comfort and energy efficiency analysis, the height and angle of GGS were suggested to be 0.9 H and 90°. This paper provided important strategies for passive building design.

Even though the control of the fan-coil system has been widely investigated, most of them only focus on the effect of room temperature control but ignore the effect of humidity control. However, both temperature and humidity are important to thermal comfort. The fan-coil is only based on temperature conditions to control the on/off of the water valve and the speed of the fan, and the indoor thermal comfort is difficult to meet the needs of the human body. In response to this problem, a simulation tool based on the calculation of the temperature and humidity of the fan-coil room was established in this work. It could realize the short-step dynamic calculation of both room temperature and humidity with different enclosure structures, internal disturbances, and outdoor meteorological conditions. In order to study the control effect of the fan-coil on the room temperature and humidity after the equipment was selected, examples of different climate zones were set up in the simulation. The simulation results showed that the temperature control strategy could meet the indoor temperature and humidity control requirements under most operating conditions. However, with outdoor low temperature and high humidity conditions, the indoor temperature was too low to reach the setting value of the water valve opening, and the humidity cannot be controlled by the fan coil. In addition, the temperature and humidity control effects of the constant fan speed and the variable fan speed control strategies were also compared. The results showed that the variable fan speed control strategy can better control indoor temperature and humidity. However, when the humidity ratio of air is too much, the temperature will be within a reasonable range but the humidity will exceed the standard.

In this study, a multi-node human thermoregulation model (Y-model for short) suitable for non-uniform asymmetric radiation environment was developed, based on the Stolwijk and JOS-2 model. The model has 16 body segments, each consisting of four basic layers for core, muscle, fat and skin, and a customizable clothing layer. The influence of asymmetric radiation on human body was considered by the model. The radiation heat transfer between the outermost layer of each body segment and the environment can be calculated separately with the inner surface of the room through the view factor, and the contact thermal conductivity analysis between the feet and the floor was taken into account to adapt to the floor cooling/heating radiation environment. In addition, sensitivity analysis method was used to optimize local parameters. The validity of the model is verified by comparing with the experimental results of human thermal response under stable and transient conditions.

There is a growing need to improve thermal comfort while reducing the energy consumption of electric vehicles to extend the driving range. In this regard, this study evaluated the thermal comfort of a contact heating system (heated seat) in both general and lower-energy modes. It was observed that a heated seat could improve passengers’ thermal comfort, even in the lower energy mode. The experiments involved eight men in their 20–50 s participating with 0.914 clo for an hour in a vehicle parked inside a climate chamber. The subjects could freely control the operational level of the heated seat. Thermal acceptability, thermal comfort, and skin temperature were measured. As a result, it was confirmed that when the operational level of the heated seat was higher in the lower energy mode, the passengers perceived a thermal sensation similar to that in the general mode.

In this study, it was investigated whether a contact cooling device attached to the back of the neck could reduce hot thermal sensation and thermal discomfort in summer. Eight men and women in their 20 s participated in experiments. They walked on the treadmill for 20 min at 32 ℃ and 50% RH environment of the room. The experimental group had a cooling device worn behind the neck, and the control group walked without the device. The thermal sensation was the lowest, and thermal comfort was the highest after 2 min of walking. After 6 min, it turned ‘uncomfortable’, and they asked to cool the device more. There was no difference between the experimental and control groups when it came to sweating.

A proximity cooling system, which reduces power consumption and improves the thermal comfort of occupants can alleviate the mileage reduction of electric vehicles (xEVs) during air conditioning, which signifies emerging problem nowadays. The purpose of this study is to investigate the effect of contact cooling on passengers by conducting a chamber experiment on 36 participants. All the subjects experienced three conditions depending on the device used. The physiological and psychological cooling effects were examined using skin temperature and subjective evaluation. During contact cooling, it was found that the cooling effect of the seat and headrest varies depending on time and body parts. Overall, this work proved the efficiency of using a cooling seat and headrest for the first 15 min and headrest cooling for the next 15 min. As there was no increase in thermal comfort after that, it would be efficient to stop using contact cooling to save energy.

The energy performance gap (EPG) refers to the discrepancy between predicted energy performance and actual energy performance in buildings. Quantifying the EPG is important for achieving an accurate understanding of building energy performance. Although previous studies have examined the EPG, there lacks a comprehensive method for its quantification. This paper aims to develop a multi-indicator framework to quantify the EPG for achieving a better understanding of the energy performance of buildings. The developed framework provides indicators from three perspectives. First, the EPG of a whole building is quantified by comparing annual and monthly simulated and measured energy consumption. Second, the dynamic of the EPG, referring to how the EPG changes over time, is quantified by analysing its variations using hourly energy data. Third, the hidden gap, referring to the hidden EPG that is neutralised by positive gap and negative gap at building energy service level, is quantified by unfolding the EPG at building energy service level. The developed framework was demonstrated using a case study with a real-life high-rise office building in Hong Kong. The results show that the annual EPG of the case building was + 4%, while the monthly EPG varied widely from − 11% to + 35%. There was a considerable variation in hourly EPG, and the variation was wider in summer than in winter. The annual hidden gap of the EPG was 7 times that of the annual EPG. The proposed multi-indicator framework contributes a multifaceted method for quantifying the EPG. The findings should help clients, designers, contractors, operators, researchers and policymakers to better understand the EPG in buildings and facilitate further strategies for closing the gap.

U of T, one of the world’s top research-intensive universities, has a large campus in the heart of the largest city in Canada. The existing buildings and utility infrastructure are aged and rely heavily on natural gas. Deep energy retrofits, a nodal plant network, and electrification are key initiatives to meet its ambitious target to move beyond carbon neutral before 2050. A new approach for energy management has been developed by revitalizing the ESCO model to leverage in-house expertise and financing combined with those initiatives to deliver regenerative sustainability solutions. Utilization of heat pumps integrated with active heat recovery systems in laboratory buildings and other buildings with high ventilation rates has become a game changer. This approach resulted in a modelled energy and carbon reductions of 32% and 84% for a laboratory building. It converts the liability of emissions reduction of burning fossil fuels into an asset by re-directing the redundant heat absorbed from exhaust air streams in cold climate conditions where considerable energy savings can be found.

With the proposal of Chinese government’s “the goals of carbon peaking by 2030 and carbon neutrality by 2060”, the utilization of low-carbon building cooling and heating energy is an inevitable way to achieve these goals. Traditional fossil energy has the characteristics of “stability and continuity”, which is not available in low-carbon renewable energy and waste heat resources. Therefore, it is determined that the application of the latter must develop a “multi-energy complementary system”. A multi-energy complementary energy system refers to a “regional energy Internet” system that embraces multiple energy and resource inputs and has multiple output functions and modes of transportation. This system does not represent the simple superposition of a variety of energy sources. But it refers to the comprehensive complementary utilization according to the level of different energy grades at the system level. And it can coordinate and arrange the coordination relationship and conversion among various energy sources to achieve the most reasonable energy utilization effect and benefits. This paper takes “ShaanGu Distributed Energy Intelligent Comprehensive Utilization Demonstration Project” as an example. There are mainly three aspects are introduced: (1) the rational construction process of the multi-energy complementary energy system, (2) the energy-saving and obtained economic effects, (3) the overall carbon emission calculation process of the project based on the premise of multi-energy complementary energy utilization.

California has a state-wide goal of carbon neutrality by 2045. Decarbonization for disadvantaged communities (DACs) poses extra challenges due to financial, informational, language, and other barriers. This paper presents the methodology, results, and analysis of energy efficiency measures (EEMs) to save energy, reduce CO2 emission, and promote clean energy access at the district scale for two DACs in Fresno, California. The methods are broadly applicable to other neighborhoods across the U.S. 22 EEMs were identified and modelled for all residential buildings in the two DACs both individually and as packages. Results show that for energy and CO2 reduction purposes, the top performing EEM package can decrease energy use and CO2 emissions by an average of 60%. For electrification, heat pump water heaters are a viable solution, coupled with air source or mini-split heat pumps. Replacing gasoline vehicles with electric vehicles is another important measure in electrification and reducing GHG emissions.

Occupant behavior is the main source of discrepancy between predicted and actual building performance, and this discrepancy is further widened in community buildings. This paper established the community occupant agent model (COAM) to generate community-level occupancy data. The model delineates the agent system boundaries from the bottom up in the perspective of complex adaptive systems (CAS). Anylogic is used to randomize and represent the decision behavior of community occupants. The case community of Xi’an is selected for the study, where the model time scale is 1 min. The measured and simulated values of the community office building occupancy were compared, and R-square = 0.8651 indicated the model’s ability to reflect the transfer behavior of actual community occupants. This paper focuses more on representing occupant behavior at the community level, and the method can provide schedule data for the urban building energy modelling (UBEM).

A full-scale experimental house to develop comprehensive passive cooling techniques for affordable apartments in the hot and humid climate of Indonesia was constructed in the city of Tegal in 2020. It is a 2- to 3-storey apartment building with a pilotis on the ground floor, consisting of 12 units with a total floor area of approximately 2000 m2. This study explains the passive design concept of the experimental house, focusing on the ventilation strategies using a vertical void, and presents the results of a preliminary field measurement of indoor thermal environments. Major thermal parameters were measured under different ventilation conditions. The results showed that the structural cooling effect by the nocturnal ventilation was apparent, but it was difficult to achieve indoor thermal comfort during the peak hours by the nocturnal ventilation alone. This implies that further additional passive cooling techniques such as radiant floor cooling and evaporative cooling are necessary. Moreover, the effect of indoor wind speed on thermal comfort of the tropical occupants should be considered more in detail.

This paper presents a new framework which fills the gap between heating technology comparison and home energy management studies by holistically considering all home energy demands. The paper answers what the cost and emissions optimal technology combinations are to meet these demands and how the inclusion of baseload and cooking demands change the technology landscape. Evaluation is completed for hundreds of heating and ancillary system combinations to show their economic and environmental potential, with optimisation using lowest net present cost (NPC) solutions. Analysis is completed for an average UK dwelling, results indicate high capital expenditure (CapEx) electrified systems result in the lowest NPC solutions and lowest emissions, ideally coupled with solar photovoltaics (PV) especially with the increased demand when including baseload demand. When including cooking fuels, Blue Hydrogen has a slight increased potential due to similar cooking efficiencies to electric cooking, but with potentially lower fuel emissions than current grid generated electricity emissions.

France has set a target to reach the zero-carbon emission by 2050. In response to this ambition, a social housing company needs help in elaborating strategies for the renovation of their housing stock. A two-level System Dynamics (SD) model is proposed to assist the company in making decisions. This novel model can not only give a general renovation orientation for the housing stock, but also provide retrofitting suggestions for each housing type. It integrates the present legislations and commonly used indicators to be more representative of the real case. The SD approach makes it possible for users to interact with the model to run multiple scenarios. The stock- level sub-model is expounded at length while the housing-level sub-model is simply discussed in this paper. A scenario generation processes is demonstrated at the end of this paper.

The purpose of this study is to assess the embodied energy emissions of walling systems used in dwelling units of India over a reference period of 50 years. The Life Cycle Assessment is undertaken within the system boundary of cradle to gate, as defined in standard ISO 14044. Preliminary work showed that the non-availability of databases for Indian materials and products makes it necessary to adopt a robust framework that deals with sensitivity and uncertainty analysis of the results. The results show the emerging walling systems that are industrially manufactured are more energy-intensive than existing walling systems. The embodied contribution by walling materials alone in these systems is 1.11–1.41 GJ/m2 which is 8.6–10.8% while overall operational energy using these systems is 11.70–11.78 GJ/m2 during its service life.

District cooling (DC) is an emerging approach to deal with heavy cooling demands since it offers substantial energy savings, up to 40%, compared to stand-alone cooling solutions. This paper focuses on DC plants and their implementation as infrastructures that include vapor absorption-compression refrigeration systems. Several DC plants in the Gulf Cooperation Council (GCC) region are functional: the anticipated penetration of DC in the GCC region will be 30% of the total cooling capacity by 2030. We address ongoing research challenges pertinent to DC plants to make the associated cooling infrastructure energy-efficient, environmentally and economically sustainable. Such challenges include the exploitation of the vapor absorption refrigeration (VAR) technology in DC plants, over the conventional vapor compression refrigeration (VCR) technology which is currently used in practice. The performance of the VAR-based DC plant can be improved by operating the VAR system with recently identified novel working fluids of refrigerants/absorbents that can be used to replace the conventional options. We propose the selection of working fluids considering both steady-state and dynamic performance. We discuss the performance improvement and cost benefits when an existing DC plant (currently driven by a VCR system) is operated with a stand-alone VCR or a stand-alone VAR or an integrated VCR and VAR system, either in parallel or in cascade configurations. Furthermore, the benefits of integrated VCR and VAR chiller plants operated with energy storage under demand variability are discussed.

There is very little information on how building ventilation systems improve indoor air quality (IAQ) during wildfire seasons. This study establishes various mechanical ventilation models to evaluate indoor particulate matter 2.5 (PM2.5) exposure levels experienced by occupants at homes, offices, and lecture theatres. The study uses outdoor PM2.5 data from the province of British Columbia during wildfire season to optimize ventilation systems to have minimum indoor exposure even during peak outdoor PM2.5 levels. Different IAQ models showed that ventilation setups with recirculated air performed better than setups with only outside air (OA) and led to significantly lower indoor PM2.5 concentrations. Testing standard Minimum Efficiency Reporting Value (MERV) filter types showed that MERV 11 and lower filters are ineffective in reducing indoor PM exposure levels.

This study aims at assessing how well a mechanical night ventilation of today, will cope with delivering acceptable thermal comfort while minimizing the electricity use and the electrical peak power demand for cooling in a historic office building in Sweden at both typical current climate and typical future climate in 2050s. The method includes numerical study in IDA-ICE simulation program using the typical current and future climate profiles. The results show that, for coefficient of performance of 3 and specific fan power of 1.5 kW/(m3/s), it would be possible to lower the electrical peak power demand and the electricity use in cooling machine by up to 2.2 kW (13%) and 1.4 MWh (48%) by night ventilation rate of 2.1 lit/(s·m2) at typical future climate in 2050s. Corresponding figures for typical current climate are 4.6 kW (36%) and 0.9 MWh (72%) owing to cooler nights and more diurnal temperature differences.

Buildings are designed, constructed, and operated with a high dependence on mechanical systems to maintain their expected thermal performance. However, disturbances such as power failure can strongly affect their thermal performance, specifically during heat waves. Building retrofit can be served as an innovative path not only for boosting energy efficiency, but also for improving thermal resilience of buildings and reducing heat-exposure risk of occupants. It is important to understand how retrofitting solutions can impact thermal resilience. However, the application of resilience quantification frameworks, in this context, is limited. Consequently, this study evaluates envelope retrofitting solutions by extending the application of an introduced novel thermal resilience benchmarking framework to a cooling dominated climate. The methodology is tested for various envelope retrofitting solutions of a single-family residential building against a four-day power failure period in Houston, Texas, USA. The phase, the hazard, and the exposure time penalties are considered to introduce a resilience labeling against the disturbance event. The results show a range of 10–62% thermal resilience improvement for various retrofitting packages compared to the existing condition of the case study in the hot condition. The findings indicate the suitability of test framework for assessing thermal resilience. Additionally, they show the importance of scalable envelope retrofitting with regards to maintaining comfort, improving thermal resilient behavior of buildings, and enhancing energy performance in the event of system or power failure.

The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people’s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial–temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk management.

Designing adaptive buildings with a satisfactory thermal performance capable of withstanding climate change shocks is as crucial as decarbonizing the built environment. Considering multiple events and their relative significance would facilitate the process of making buildings more adaptive against shocks. This study used a simulation-based method to quantify resilience KPIs, annual energy performance, and life-cycle cost while considering multiple outage events. Due to the results, emphasis was placed on the ice-storm event (relative weights of considered ice storm and heatwave are 0.85 and 0.15, respectively). Also, the thermal resilience improved by a factor of two in the as-built case compared to reference (code-minimum) case. The payback period was reduced from 13 to 5.2 years considering the impact of both ice storm with a 10-years frequency and heatwave with a 3-years frequency. Further studies should consider more events and identify upgrade packages using an optimization approach.

Quality metrics such as reliability, resilience, robustness, and vulnerability (3RV) all assess building performance from different perspectives. These concepts have overlaps that make it challenging to distinguish and quantify them properly. Using a survey, this paper investigates how researchers and industry experts applied 3RV concepts to building thermal performance. After reviewing developed definitions in literature, survey outcomes are presented to: (i) clarify respondents ideas about the basic features of 3RV concepts and evaluate the significance of each 3RV concept compared to other features of High-Performance Buildings (HPBs), (ii) estimate respondent interpretations about the HPBs thermal performance using 3RV, and (iii) quantify the relative importance of 3RV concepts based on event types and building archetypes. Survey outcomes indicated that there is an inherent bias toward resilience and relatively less awareness of how these concepts overlap significantly that could be solved using a central framework.

This study is framed around two research questions to (1) investigate the probable changes in future climate and (2) evaluate the changes in cooling demand of a studied building when implementing an assemble climate representing mid-term future period (2041–2060). The chosen building is a preschool in central Sweden that fulfills the Nearly-Zero Energy Building (NZEB) requirements based on today’s Swedish National Building Regulations. To assess and cope with the present and future cooling energy needs of the building, a climate file representing present conditions along with a projected future typical climate file are utilized. The future climate is an assembled typical meteorological year climate file using the CORDEX data. The present climate file underpredicts the future energy demands therefore verifying to be unsuitable for anticipated energy analysis. It was discovered that the cooling demand for assembled climate file is almost 4 times the present climate file for the studied conditions.

This paper presents, to our knowledge, the first system-level engineering study to bio-mimic the cybernetics and flow dynamics of energy resources in natural ecosystems for the control of heterogeneous energy infrastructures in the built environment. To this end, we introduce a novel Biomimetic Pulsing State (BPS) control that functionally mimics mature ecosystems. A preliminary Modelica-based case study features a single-family residential building with electrical and HVAC subsystems. The BPS control objective is to minimize the energy exchange between the building and the grid for the purposes of future self-supporting buildings and grid stability. The building contains PV, a wind turbine, a battery storage system, and a fan coil/heat pump HVAC system served by an ambient district energy network. Evaluating the control performance (BPS vs. constant setpoint) over several renewable energy scenarios (net importer, net zero, net exporter), simulation results show how the building’s HVAC system can dynamically adjust its electrical load and temperatures to the electrical system’s net energy status in real-time with BPS control. As a net importer, the heat pump consumed 29% less energy and its peak power reduced by 15% with BPS control compared to the constant setpoint case, with the zone air temperature 1°C lower on average. As a net exporter, the heat pump effectively consumed the same energy, but the peak power increased by 34% with BPS control, while the zone air temperature was 1°C higher when renewable energy was abundant, preheating the home. BPS and constant setpoint control produced comparable results under a net zero scenario. While further evaluation is essential, BPS control may help communities meet their sustainability and resiliency targets as they transition towards fully distributed and renewable energy grids.

The global scientific consensus associates 38% of the climate impacts to the building and construction sector. This paper discusses the primary climate physical risks faced by the Milan Innovation District (MIND). This development is piloting the transition to resilient green urban planning. It also strives for a high-quality urban realm that provides comfortable outdoor spaces that helps to enhance the experience of people visiting the site now and in the future. The site resilience strategy was defined with a three-step methodology: (a) baseline definition for outdoor comfort conditions for the site and of the standard effective temperatures for primary outdoor functional areas; (b) use of scenario planning, RCP 4.5 and RCP 8.5 at 2090, to project and understand future climate impacts on project’ performance and (c) climate change adaptation and resilience planning workshops to discuss analysis results and to identify functional distribution de-risking strategies to mitigate the impact of extreme temperature and heath wave. Climate projections data are from the World Bank Change Knowledge Portal and the projected. epw Weatherfiles for Milano Linate generated using IES and Weathershift tool. Results, depending on the emission scenario selected, indicated up to 3–7°C temperature, up to 10–20% probability of heath waves, up to 12–24% increase in the 24 h max precipitation and up to 8–15% increase in the 5 days max cumulative scenario. Analysis included masterplan’ sunlight, spatial thermal comfort, and the climate change visualization. These facilitated the understanding of how passive measures could help improve comfort. The SET* analysis on the west gate portion of the masterplan design helped to model the thermal comfort conditions and to define the directions for improving outdoor comfort performance. The analysis considered a peak day and is therefore conservative. This work led to mitigating actions to improve outdoor comfort. Also, it underlines the importance of climate risk assessment in the early urban planning stages to create communities that are adaptive and resilient to the impact of climate change in future conditions.

Defining and quantifying building thermal resilience is important given that this helps determine the capability of a building system to tolerate disturbances from exposure to extreme heat events, and from which to compare different retrofit strategies to enhance the robustness of a building system to such events, and as well, to estimate the rapidity of recovery from heat events that increase the likelihood of having a comfortable indoor environment. In this study, building thermal resilience to summertime heatwaves is defined based on the concept of a resilience trapezoid. The Thermal Resilience Index (TRI) and its labelling class (Class F to Class A+) are proposed as a means to quantify the resilience level in respect to the relative improvement from original and adequate indoor comfort conditions. The distribution of resilience at the Zone level is analysed and is a focus of the research on building thermal resilience. This framework is demonstrated with an 5-storey long-term care building before and after retrofits, the retrofits used to render the building more resilient to heat events. Using four different passive strategies to mitigate the effects of extreme heat events, the highest resilience level that can be reached is Class B, which provides an improvement of 50%-70% in the degree of resilience. From the work completed in this study it is shown that the TRI index is useful in assigning the overall and spatial resilience of a building.

Homebuilders constructing in areas that experience natural disasters (such as wildfires, hurricanes, tornados, high winds, earthquakes, floods, severe winter weather, and high termite infestation probabilities) are sometimes faced with conflicts when addressing potential risks from natural disaster risks while also incorporating energy-efficiency details into their homes. This paper presents several such conflicts that were identified from multiple data sources, including a 2-day workshop on the nexus of disasters and energy efficiency hosted by Oak Ridge National Laboratory, a comparison of publications on disaster-resistant and energy-efficient construction, and feedback from visitors to the Building America Solution Center, a U.S. Department of Energy-sponsored online resource for the home building industry. The paper identifies solutions that can alleviate these conflicts and discusses challenges that must be overcome to foster adoption. The benefits of homes that are simultaneously disaster resilient and energy-efficient must be explained to home buyers, the insurance industry, and builders.

Man-made climate change is real and about 40% of this risk is associated with the building sector. The Milano Innovation District (MIND) aims to be an urban pilot where to test the green transition proposed by the European Commission bringing together the public and private sector. Beyond this ambition there is also an objective to be absolute zero carbon by 2040 no offsets. This paper discusses the methodology to achieve the absolute zero carbon ambition, as a key element of the overall sustainability strategy for the development, at the infrastructure and building level. The carbon strategy was established in two stages: the first stage, based on the approved urban plan data, modelled the carbon baseline per main carbon themes (i.e., energy, mobility, water, and embodied carbon), set carbon reduction targets and identified recommendations to achieve the zero carbon targets. After verification of the feasibility of the reduction targets with the market and the designers there was a second stage. The carbon baseline was remodelled considering the development phasing, more precise boundaries of control and impacts of Stage 1 reduction targets. The first stage led to a combined 38% potential carbon reduction. The second stage, because of the energy strategy and of the design carbon budget implemented, led to a 78% reduction and 85% if not considering transport directly controlled by the public administration. The elements considered in the carbon baseline proved that MIND fully supports the European policies being introduced for green loans (European Investment Bank (2020) European Investment Bank Climate Action. Eligible sectors and eligibility criteria) in terms of energy performance, resource management and mobility.

Open spaces may be the alternative for interventions in the consolidated urban context providing resistance to flooding, heat waves, an extension of the dry season, and other consequences from anthropogenic actions. The aim of this study is to elaborate some proposals of interventions for open spaces in Aracaju and to contribute to making it more resilient to those events. Aerial views, drawings, local visits and images were applied to characterize each open space. Following this, punctual proposals were elaborated according to restriction of urban density of the built environment as rain gardens and permeable surfaces on squares. This study may offer an alternative to municipalities to provide more resilient spaces for the city.

This paper aims to evaluate the design measures of a community hub in the Canadian province of Ontario through the thermal resilience metric of passive survivability. The frequency of power outages as a result of climate pressures are increasing and community centres are relied on as areas of refuge. Therefore, designing these buildings with passive survivability in mind is important, and challenging as a result of the high occupancy and fresh air requirements. The study compared typical construction (Ontario Building Code) to high performance façade designs (Zero Carbon Building) using whole-building energy simulation. Unsurprisingly, the ZCB design showed significant energy savings over code, but the passive elements required in zero carbon design also provided improvements to thermal comfort and passive survivability in both hot summer, and cold winter conditions. Enhanced solar gain control and natural ventilation provide additional performance benefits.

Extreme weather and climate events strengthen the frequency and magnitude of natural disasters, the most threatening risks to human life. To understand how climate change affects disasters, an improved predictive model of natural disasters based on the Non-homogenous Poisson process was developed. Using historical events and data from BC180 to 2019 in China, an intensity function curve, expressed by the Fourier series expansion, was fitted with a 95% confidence interval giving a distinct result scale. It showed that natural disasters do fluctuate periodically and are significantly impacted by environmental factors, such as sunspot activities. Currently, the earth is in its peak phase, implying a probabilistic increase in the future occurrence of natural disasters. Although limited by historical disasters data and the analysis of humans’ positive efforts to climate change, the research may provide the expected frequencies of natural disasters to guide in building mitigation and resilience.

Compound optimization of distributed energy systems, urban morphology, and the transportation network is crucial to improving the robustness of interconnected urban energy infrastructures and enhancing their resilience to extreme climate events. Available methods and tools mainly focus on optimizing one component in urban areas and fail to consider complex interactions in interconnected infrastructures. This study introduces a compound optimization methodology that optimizes the energy system in connection with urban morphology and electric vehicle (EV) charging demands. In this regard, the energy demand of five multi-functional urban neighborhoods is assessed and optimized considering 13 climate scenarios (2010–2099). Results showed a significant improvement in autonomy level and a notable reduction of infrastructure costs (over 40%) by linking these three sectors. It is also shown that energy demand can increase up to 17% in extreme weather conditions, leading to over 30% infrastructure costs.

This work highlights the need for accredited engineering curricula to integrate mandatory climate-related learning and proposes four high-level competencies for consideration. The work surveys ten accredited civil engineering programs in Canada as a case study. The survey result that accredited civil engineering programs provide little to no training toward climate competency underscores the need for greater attention to engineering curricula as a tool by which the engineering profession can begin playing an active role in addressing the climate emergency. Discussions touch on the implicit inclusion of climate competencies in other courses and how this implicit inclusion could become more explicit by looking to medical programs’ approach. Discussion also touch on potential challenges arising from inconsistent degree requirements in engineering programs and the engineering culture that centres objectivity.

Climate change has led to prolonged, more frequent, intense, and severe extreme weather events, such as summertime heatwaves and thus, it is imperative to understand and evaluate the overheating conditions. This study evaluated a reference year selection method in terms of typical and extreme reference years based on future climate datasets to assess both outdoor overheating in the future. The future climate data were collected from the Coordinated Regional Downscaling Experiment (CORDEX) program. A Canadian city, Montreal, was selected for the overheating evaluation during three selected periods (2001–2020, 2041–2060, 2081–2100). This study demonstrates that the reference year selection method could reasonably capture both typical and the severest overheating conditions. In contrast, neither the severest nor the typical yearly outdoor and indoor overheating conditions could be predicted by the design summer year method. Finally, owing to the effects of climate change, the maximum and average yearly overheating hours increased by 1–2 times until the mid-term future (2041–2060) and by 2–4 times for the long-term future (2081–2100), as compared with the values for the contemporary term (2001–2020).

This paper investigates the needs and expectations of both planners and clients, identifying the main barriers to the implementation of climate adaptation software tools. It also seeks to identify the main issues on software compatibility and performance efficiency. This is achieved via the analysis of process maps produced in objective experiments with different climate adaptation tools implemented in a case study project—a sustainable neighbourhood in the city of Ulm, Germany. The ISO 25010 framework is implemented to investigate the advantages and disadvantages of these tools, including: functional suitability, information quality, reliability, performance efficiency, usability, and compatibility. The results show that current climate adaptation software tools are faced with some limitations, including a long simulation process, low interoperability with other planning software tools, and an inefficient implementation process.

The effect of intensifying the urban greenery cover on urban comfort, energy, and health was monitored. A novel approach was followed by integrating microclimate simulations and clustering analyses to define the impact of the heat mitigation technique on the urban environment and community and to correlate the environmental and health responses. The application included increasing urban vegetation, tree canopy, and buildings’ greenery. The results ensured the benefits to ambient conditions and energy consumption while considering the application cost. The proposed approach was able to expect the enhancements in community health records related to elderly cardio-respiratory mortalities due to reported enrichments in the urban environment.

The demand for heat accounts for approx. 50% of the total energy demand. To achieve CO2 – neutrality, it is important to find ways to reduce this demand for heat. German communities with over 20,000 inhabitants are required to have plans to cover their heat demand. Meeting heating requirements using District Heating (DH) networks proved to be a good and effective solution. Therefore, this study describes a standardized method for fast and accurate estimation of potential DH networks to plan a cost-effective coverage of existing heat demand. The GIS-based methodology considers heat demand values from different available sources, based on which distribution pipes are generated. The results show grid-based heat demand values with its corresponding DH networks generated using ArcGIS Network Analyst and calculated using the community of Rastatt as an example. This method proves to be a good instrument for the first step of District Heating Planning.

Air pollution is an environmental problem of major concern generated by its adverse effect on human health. Due to the high traffic density, cities often face increased concentrations of air pollutants in comparison with its surroundings. Inequitable distribution of environmental impacts within a city or region may raise issues of “environmental justice”. Most time, series studies are based on entire cities and spatially averaged air quality, in order to maximize statistical power. Environmental justice refers primarily to exposures; note that the official definition does not relate to excessive vehicular or domestic emissions that may be an inevitable result of being poor in an urban setting. Additional exposure differentials may result from differences in housing quality, in terms of air exchange rates and the presence of indoor pollution sources. An important point to be made here is that, to the extent that outdoor air quality may be implicated, the culprits are much more likely to be local primary pollutants like CO or SO2 than the more widespread secondary pollutants like PM2.5 or O3. A case study was conducted in European Union on the Romanian capital, Bucharest, in order to identify discrepancies between different regions of this city, while correlating the level of air pollution with different types of neighbourhoods. For example, one of the correlations was found between air pollution and vulnerable neighbourhoods’ location, due to lack of proper heating measures, lack of green areas and an increased traffic. At the city scale, this aspect is not noticeable, but it counts the uneven measures taken in the city by people, NGO’s, communities, and politics for reducing the air pollution.

The present work investigates the ventilation performance of a compact area in Rome, Italy, performing 3D steady-state Reynolds-averaged Navier–Stokes simulations for twelve wind directions. The urban ventilation is expressed in terms of the spatially averaged mean wind speed ratios at pedestrian level, γped, and at 10 m height, γ10. The urban morphology is quantified using morphological parameters (MPs). The relations between each MP and γped and γ10 are expressed through simple models obtained using linear regression analysis. The relations present strong correlations with R2 up to 0.89. The provided linear models can be valuable tools for highlighting areas potentially vulnerable to poor air conditions without running simulations.

Urban vegetation has a large impact on urban microclimates and is an important element in urban environmental design. Its representation within state of the art urban climate models provides new capabilities for urban planners and designers to consider the impact of trees on urban climates. However, assessing the climate impact of urban vegetation and evaluation of these models at the street scale demands spatial sampling of the important driving (solar and longwave radiation) and response (surface and air temperatures) variables at high spatial resolution. This project describes a mobile traverse methodology (MTM) capable of measuring, for the first time, representative values of radiative fluxes and surface temperatures within residential street canyons with complex vegetation assemblages. Traverses were conducted at select times over a day in three neighbourhoods with varying street tree canopy cover in London, ON, Canada. The system can differentiate unique spatial patterns of measured radiative fluxes and road surface temperatures between neighbourhoods with varying tree canopy cover. The associated distributions of $$T_{Road}$$ T Road and incident radiative fluxes provide a unique dataset to test numerical models that resolve within street-canyon vegetation.

The purpose of this study was to investigate the variation of outdoor thermal comfort (OTC) across open spaces in different Toronto neighbourhood archetypes that are distinguished from one another based on their levels of densification. Simulations were carried out to determine the Universal Thermal Climate Index (UTCI) at 10:00 am, 12:00 pm and 4:00 pm on representative extremely cold and hot days. The neighbourhood morphology impacts the magnitude of solar radiation intensity incident on the outdoor surfaces; hence, this plays an important role on the level of thermal comfort in public spaces. The results of the study showed that the OTC in the Downtown neighbourhood was uncomfortable during winter, as high-rise buildings effectively blocked solar exposure to public spaces below. In comparison, other neighbourhoods, which were relatively less dense, had better thermal stress levels for the same period. This study provides a context to the advancement of solar access guidelines in Toronto.

The sun irradiation of building is one of the important factor which influences the urban heat island effect. Cool and reflective surfaces permit to reduce the local temperature. Infra-red reflectance measurement using a spectrophotometer is the most used method to evaluate the effectiveness of “cool coatings”. However, the necessity of the evaluation of the real behaviour is very current. The purpose of the work is twofold. Check if simple temperature measurements allow the comparison on the thermal behaviour of organic coatings and secondly to identify which is the source of artificial light that best simulates the solar radiation. The properties of NIR reflective pigmented organic coating and the references are evaluated using three different radiation sources (incandescent infrared emitting lamp, tungsten halogen one, and xenon arc one) in comparison to the natural solar exposure. The thermal performances are evaluated by a roofless box where the panel and the internal box temperatures are checked by two thermocouples and the surface temperature by a IR thermo-camera. The xenon arc lamp best simulates the behaviour of roof coverings irradiated by natural solar radiation. According to temperature and reflectance data, the studied NIR pigmented coating shows a comparable behaviour as TiO2 based coating.

The study presents a cost-effective and scalable method to determine the Window to Wall Ratio (WWR) and Air conditioning status of existing buildings from ground-view façade imagery. Object Detection Classifier deploying Faster Region-based Convolutional Neural Network (Faster R-CNN) is used to detect windows and buildings in visible images. The detected elements are used by the second algorithm to calculate the Window to Wall Area Ratio (WWR) with a 40% variation from actual values. By superimposing the detected elements on corresponding thermal images of the building, a third algorithm is used to obtain the outside surface temperatures of windows and walls. Based on simulation study, a difference greater than 7 °C between these values translates into air-conditioned zone.

More accurate tools are required to replicate urban climates to achieve healthy and comfortable urban environments. To this end, this study implements a novel high-resolution simulation framework to improve the OTC modelling by dynamic coupling of convective fluxes calculated by computational fluid dynamic (CFD) model, and dynamic building energy simulation (BES) for analyzing outdoor surface temperature of buildings. In addition, radiative fluxes emitted from building surfaces are coupled with latter models. The workflow is applied at the Grasshopper platform based on the results of ANSYS Fluent as the CFD and EnergyPlus as the BES tools. This framework is tested within a generic case study representing an urban neighbourhood. As a result of this framework, tempo-spatial values for OTC are achieved at each time-step of simulation and then compared with the OTC values from the traditional OTC modelling approach. Statistical analysis of results shows that the OTC valued predicted using the coupled method can change considerably compared to OTC results from traditional methods at the neighbourhood scale.

In the context of global warming, cities need to be prepared to limitate thermals stress in city, especialy during heat wave, when the it’s become to be a health issue. This study is focused on evaluating the cooling effects of a pervious, ochre pavement combined with pavement-watering in response to the urban heat island effect and extreme heat events to offering permanent cooling effect and stronger effect with water mitigation, althougth punctual. A micro-climatic study was conducted following a Before After Control Impact (BACI) design to evaluate the effect of the new pavement and watering on pedestrian heat stress during typical heat-wave conditions. The surface temperature of the 100 m2 test site was also monitored. Since the pervious pavement have a low albedo, we see no significant effect compared to standard pavement althougth with the use of water we are able to see a cooling effect of − 15 °C on the surface imputing a − 1 °C refreshing effect on air temperature at 1.6 m height.

Standalone Urban Canopy Models (UCMs) attempt to replace the full-fledged atmospheric representation with a computationally light equivalent. The UCM used in this study focuses on the overall exchange of heat, momentum, and moisture with the first level of the atmosphere right above the urban canopy. This enables us, in an urban design/retrofit context, to conduct year-long simulations relatively quickly and to assess multiple design alternatives in terms of their impact on urban heat island, outdoor thermal comfort and/or buildings energy demand. To force the UCM at the top boundary of the domain, we investigate two approaches in this study: (1) year-long hourly measurements collected at a nearby rural weather station inform a single-layer urban boundary layer model immediately atop the UCM using a highly original and accurate mechanism of action that differs from the state of the art; (2) direct forcing of the top boundary of the UCM by year-long hourly atmospheric data provided by the ERA5 time series for the location of interest. Furthermore, the urban domain can be divided into multiple categories, each having different morphologies and thermo-physical properties. The result is a computationally light numerical model of the urban canopy air that can be seamlessly incorporated in a design optimization work flow. The model is applied to the city of Abu Dhabi (United Arab Emirates). For both boundary conditions, the model’s accuracy is validated against measurements.

In this work we employed a sol–gel process to produce YIn0.9M0.1O3-ZnO pigments (where M is Mn, Cu, Fe). These pigments showed high reflectance in the near-infrared region, proving their potential use as pigments for cool coatings. By thermal treatment the crystallinity and the structure of the YIn0.9M0.1O3 changes, thus proving that it is possible to tune NIR reflectance efficiency and color by changing M and the calcination temperature. The difference in terms of colour, structure and NIR reflectance are attributed to the different YIn0.9M0.1O3 that are obtained.

The urban overheating is a well-documented phenomenon caused by the synergic action of local and global climate change. Increasing the albedo of cities by means of cool materials is an effective strategy. This paper explores the building cooling saving potential of large-scale application of cool materials through the case of a temperate oceanic metropolis: Melbourne, Australia. A mesoscale model is implemented and validated using the calculation platform WRF, creating reference and modified albedo scenarios of the two hottest summer month. The weather data feeds a dynamic building energy model and the potential energy savings are calculated. Energy savings up to 2.2 kWh/m2 and 31% are calculated, moreover the city scale application over performs the single building application by 0.7 kW/m2. The analysis of spot results show that the urban albedo modification strongly reduces the cooling energy use of buildings in the warmer inner city compared to sea-breeze mitigated coastal areas.

Residential district is the main type of urban land coverage, and the large scaled development with high density has a huge impact on the urban climate. In China, the methods of residential district design are diverse, which creating different urban typology and providing different effects to the urban thermal environment. This study aims to explore the thermal environment optimization strategies of residential areas with different development intensity (plot ratios) from the perspective of urban renewal and residential district design in Xi’an, China. In this study, residential areas with low, medium and high plot ratios in Xi’an city were selected for field measurements and environmental simulation for five proposed optimization strategies. By comparing the air temperature, mean radiant temperature and physiological equivalent temperature at the pedestrian height, the thermal environment optimization texture of each strategy was explored. The results show that the same strategy brings different optimization effects in different residential districts with different development patterns. The conclusion of this study provides suggestions for the optimization and reconstruction of residential areas, contributes to future residential area development and design.

Thermochromic (TC) coatings for buildings represent a promising solution for temperate climates to reduce the cooling energy need without occurring in heating penalties in wintertime. Nevertheless, only few solutions able to cope with rapid photodegradation are available. For this reason, in the last few years, a growing interest in the development of TC solutions unaffected by photodegradation was reported, and new products are expected in a short period. In this study, a TC coating based on Leuco dye was applied on a flat-roof and the variation of the outer surface temperatures and solar reflectance was monitored for three days. A spectrophotometric characterization of the thermochromic samples was conducted in their coloured and colourless phases, before and after the outdoor exposure. The results of the study provide insights on the energy saving, on the urban climate mitigation potential of TC coatings and on the photodegradation occurring when exposed to solar radiation.

A tracer gas measurement method was developed to investigate the single-side removal process of pollutants through the roof level, conducted in a wind tunnel utilizing a deep box model with various openings at its top. A homogeneous emission was modelled by filling the box with well-mixed nitrous oxide released from a line source inside. The removal efficiency from the openings with box at different orientations was evaluated by normalized purging velocity (PV) and spatial-mean age of air ( $$\overline{\tau_p }$$ τ p ¯ ). The PIV system was adopted to visualize the flow field above the opening. Varying interactions between the perturbations from the openings and the deflected flow over them resulted in diverse PVs at different orientations. There are also clues showing that the Reynolds-number independent conditions for cavity flows, such as inside a street canyon, are also related to the scale of the open area at the roof height.

Many studies have proved that urban morphology (UM) and anthropogenic heat emission (AHE) have significant effects on urban microclimate. However, existing research has mostly focused the effects from UM on microclimate or the effects of AHE on microclimate respectively. Therefore, this study quantifies the relationship among UM, AHE and microclimate, using correlation and regression analysis based on field measurement data of air temperature (AT) in summer in 5 typical urban blocks of Xi'an, a high-density city in western China. Results show that AHE explains more than 70% of the hourly AT variation. In addition, the greening cover ratio (GCR) is the strongest index, which helps to cool down the AT and reduces the AHE. 10% increase in GCR decreases the average AT by 0.43 °C and AHE by 118 W/m2. This study established a comprehensive model for the impact of UM and AHE on microclimate and provides guidance for developing cooling strategies in high-density urban areas.

The number of high-rise buildings was kept increasing in cities. When estimating the energy demand of high-rise buildings and urban blocks, taking the effect of urban heat islands but not the vertical meteorological patterns into account may bring deviations. We quantified this deviation in this paper. First, the vertical air temperature and wind profiles of Xi’an city were constructed with the measured data. The winter (summer) urban air temperature gradient reached 0.8 °C(0.1 °C)/90 m in the nighttime and − 0.7 °C(− 1.3 °C)/90 m in the daytime. The urban wind speed gradient was 0.8 m/s(0.4 m/s)/90 m in winter (summer). The meteorological data gradient of cities is smaller than that of suburbs. Second, we proposed 42 representative high-rise urban block models for energy simulation. The results showed that ignoring the vertical urban meteorological pattern may cause the underestimation of the heating load of urban blocks by 26% and the overestimation of the cooling load by 2%. This paper offered a reference for estimating the energy demand of the high-rise urban blocks.

A granular understanding of urban electricity demands is a prerequisite for defining measures towards zero-carbon cities. Previous efforts focused mostly on residential electricity demands. This work introduces a bottom-up method to assess non-residential building electricity demands based on 3D building models. First, 200 + non-residential usage types are implemented via a Usage Library. For three of these usage types, specific electricity indicators (SEI) are subsequently calculated and the annual electricity demand is determined based on the SEI and the heated area per building. The new method is validated with 2019 data from two German cities: while simulated electricity demands for individual buildings can deviate by more than 50% compared to measured data, aggregated results show much lower deviations of 12% for office buildings, 10% for day-care centers, and close to 0% for school buildings. The proposed method can thus provide good estimates for various important non-residential building types at city quarter level.

In the aftermath of the 2003 heatwave, and with growing concern over climate change, Paris City Hall has been implementing several heat mitigation strategies. One of these is the OASIS Schoolyard Strategy which aims to transform Parisian schoolyards into cool islands. Within this framework, the EU-funded ERDF UIA OASIS Project aims to study the transformation of ten schoolyards, including an evaluation of their microclimatic performance. The present article presents case study results from one schoolyard using GIS data and fixed and mobile microclimatic measurements. An analysis method for mobile measurement data is proposed, tested and discussed on the basis of this case study.

In the aftermath of the 2003 European heat-wave and within global climate change awareness, strong interest has risen within the City of Paris towards urban cooling. Several experiments have been carried out including pavement-watering since 2013. More recently, this method was experimented on the three test sites of the LIFE “Cool & Low Noise Asphalt” project. This project aims to study the performance of pavement-watering on innovative pavements to reduce pedestrians heat stress in the summer, since 2019. Depending on the pavement, statistically significant air temperature reductions of up to −0.4 °C on average were observed in addition to reductions of up to −0.7 °C on average of UTCI-equivalent temperature. The impact of resurfacing the pavement and the combination of resurfacing and watering are also studied.

Weather normalization is essential when conducting building energy benchmarking or comparing the energy consumption of similar buildings in different climate regions. Degree-days, which is derived from outdoor air temperature, has conventionally been used as an index to represent the weather condition in normalization. However, both air temperature and humidity affect the energy demand of a building cooling system. Existing literature considered the impact of separate weather parameters such as solar radiation and wind speed on weather normalization. This paper aims to investigate if Specific Enthalpy of Humid Air, which is a function of both outdoor air dry-bulb temperature and moisture content, can be used as an integrated indicator of weather condition in place of cooling degree-days to normalize long-term cooling energy consumption more accurately. Cooling Enthalpy Hour, which is calculated at each hour of weather conditions above a reference enthalpy value, is proposed to be the indicator. To test this hypothesis, simulated hourly energy consumption data was normalized using both Cooling Degree Hour and the proposed Cooling Enthalpy Hour as representation of weather variation. Preliminary results showed that the proposed method derived more consistent energy consumption irrespective of weather variation. This demonstrates that Cooling Degree Hour is not highly representative of the varying weather condition that should be normalized from the data.

Abu Dhabi is an important city that has gone thru major urban transformation over the last 50 years. These changes impact the development of neighbourhoods, especially residential neighbourhoods. The morphology of this urban blocks also impacted the heat island effect (UHI). UHI is one of the contributors to climate change. Therefore, having sustainable neighbourhoods means reducing the UHI and make cities more liveable. The methodology to be followed in this study is as per the below steps: (1) Residential blocks in Abu Dhabi. Analysis of current conditions & Proposal for UHI reduction thru different strategies. (2) Modelling and Simulations. Create and analyse the models in Rhino Grasshopper. (3) Findings and results. The aim is to analyse the current conditions of the selected neighbourhoods in Abu Dhabi. Based on this analysis the research focus is to understand which strategy can reduce more the solar radiation on the streets of the neighbourhoods, therefore reduce the surface temperature and have an impact cooling down the city. Based on the findings different strategies can be applied in the residential blocks to reduce the surface temperature of the streets and buildings, therefore reduce the UHI effect. Having sustainable neighbourhoods has a direct impact in making cities more liveable, walkable and improve the climate change. The main tools to be used are rhino/grasshopper. Thru advanced software, the findings can be optimized and contribute to more sustainable neighbourhoods.

To select a typical meteorological year (TMY) for those regions lacking long-term recorded meteorological data, a simplified Sandia method requiring for only four weather parmeters is proposed. A low-latitude island in China was selected as a case. Based on the measured weather data from 2005 to 2014, the monthly energy consumption of a typical office building model was simulated. Then, the Pearson correlation analysis was performed between building energy consumption and daily means of dry-bulb temperature, dew-point temperature and wind speed and daily total horizontal radiation, respectively. Consequently, the weighting factors of each parameter were determined according to the equal ratio of correlation coefficient. Compared with Sandia method, the normalized root mean square error (NRMSE) of energy consumption based on TMY selected by the new method with simplied parameters decreases from 3.30 to 3.12%, which validates that the proposed method has reliable accuracy in TMT selection.

The Energy Bus System (EBS), a bidirectional low temperature heating and cooling system, is expected to become the next generation of energy systems. It is a novel concept that promises less heat losses and better utilization of renewable and waste heat sources. In this paper, performance analyses of an energy bus system, consisting of borehole heat exchangers, a cooling tower, surface-water heat transfer coils and heat pump systems, and distribution network for residential community in the downstream of Yangtze River has been conducted. Thermal and energy performance of the proposed system adapting different pipe networks and different multi-source control strategies were evaluated using simulation models in Modelica. It was concluded that ring form pipe network with multi-stage pumps and dynamic optimization control can help improve the economic and energy efficiency of EBS.

The relationship between corrosion damage and salinity concentration transported by the wind flow around a building was investigated using computational fluid dynamics (CFD) applied to a large sports stadium as a case study. First, a CFD-based estimation method for salt deposition resulting from wind flow was developed and applied to a large sports stadium using local meteorological data. Next, the estimated salt deposition results were compared with the measurement data of the corrosion damage, and the estimation accuracy of the proposed approach was evaluated. The areas with high salinity concentrations predicted by the CFD simulation and the areas where the corrosion damage actually progressed were in good agreement. It was confirmed that CFD is useful for considering effective countermeasures against such damage.

In this study, the synergistic interactions of the urban heat island effect and the heatwaves occurring in the summer of 2018 in the cities of Montreal and Ottawa, Canada, are discussed through a comparison between days with and without a heatwave event. Three (3) time frames were prepared as part of this comparison: a pre-heatwave period from June 24 to June 29, 2018 (6 days); a heatwave period from June 30 to July 05, 2018 (6 days); and a post-heatwave period from July 06 to July 11, 2018 (6 days). The urban climates of these two cities were simulated using a Weather Research and Forecast (WRF) model at a grid resolution of 1 km; the urban heat island intensity was calculated using two methods: (i) calculating the temperature difference between urban and rural grid cells at a distance ranging between 3 to 10 km to the boundary of the urban area; (ii) using the “urban increment” method by which the temperature of urban grid cells are compared to the results of another simulation having all the urban land cover replaced by cropland. The diurnal evolution of several near-surface variables was compared throughout these three periods, including the ground surface temperature, the 2-m air temperature, relative humidity, and the 10-m wind speed.

With rapid urbanization and increasing population in the last decades, more constructions have been built in the urban region, especially large cities. Therefore, researchers pay more attention to the interactions between urban microclimate and city dwellers. This paper presents the recent research progress on urban microclimates, mainly in the latest ten years from 2010 to 2020. 563 articles were reviewed, covering the investigation approach, different scales of investigation area, and topics. In addition, the development of traditional methods, including field or onsite measurements, wind tunnel modeling and computational fluid dynamics (CFD) simulation, and emerging methods such as artificial intelligence models, were discussed. Finally, this paper also reviewed the progress on key findings on the related topics about the urban environment, including urban wind energy, urban wind comfort, urban heat island, outdoor thermal comfort.

Due to computational barriers of computational fluid dynamics (CFD) models, they cannot be used for tasks such as (near) real-time simulations. Reduced-order model (ROM) can be used as an alternative to CFD since it can approximate the results in a fraction of the CFD simulation time. The present article generates a data-driven ROM, using convolutional autoencoders (CAEs) and long short-term memory (LSTM) networks, to reconstruct the turbulent flow field within a simplified urban area. Furthermore, the effect of the kernel size on capturing spatial information is investigated. The results indicate that, although the model has some deficiencies in the flow field reconstruction in high-gradients regions, the model's overall performance is acceptable. Moreover, it is shown that the kernel size has a negligible impact on the model performance for the present model and dataset.