Smart Cities

One of the most important things in a microgrid is the real-time measurement of all its elements, whether they are consumers or energy producers so that at the end of an established period, the total balance of production-consumption is carried out. It is at this moment when the energy distribution company and its costs become important. Focusing on it, a measurement system based on an infrared sensor and Arduino has been developed, to which a specific software is installed that allows obtaining the value of the instantaneous power consumed by the microgrid from the reading of the LED indicator of metrology of the meter of the distribution company with an error less than 1% daily. This means an important improvement in the knowledge of the energy consumption of the microgrid and implies an advance in the understanding of the electric bill allowing reducing its cost in the contracted terms.

Various organizations have reported that buildings (among them, educational institutions) are responsible for the consumption of 40% or more of all the primary energy produced worldwide. The control of temperature and lighting in said institutions is carried out manually. That means that every time a classroom is used, people must turn on lights and air conditioners, and then take care of turning them off whenever they are not required. Faced with this scenario, the alternative proposed in this work allows efficient automatic control of lighting and temperature preferences for each professor and each class.

That is why a Prototype of Classroom Energetically Efficient was built in this paper. It has three modules: The one is the web application that was developed using the Laravel framework for the backend and Vuejs for the frontend. Its main function is to send commands to devices. The second is the IoT framework, which fulfills the function of communicating the web application with the hardware, providing the necessary endpoints, and making the registered data available. And finally, the hardware that was built using NodeMCU ESP8266 boards. Its function is to be an actuator i.e. receive the data from the IoT framework and executes commands. We also build a classrom mockup to show the prototype in action.

Also, the performance tests of different scenarios were carried out, being satisfactory, and allowing the development of the planned functionalities.

REMOURBAN is a large-scale demonstration project whose main objective is to accelerate the urban transformation towards the smart city concept considering all aspects of sustainability. For this purpose, an Urban Regeneration Model has been developed and validated in the three lighthouse cities of the project (Valladolid-Spain, Nottingham-United Kingdom and Tepebaşı-Turkey). REMOURBAN has carried out different interventions in the city of Valladolid with the aim of transforming it in a more sustainable and smarter city. These actions have been evaluated using the evaluation framework developed within the project, to know the real impact of these interventions in the project area and to transfer the knowledge to other cities that want replicate these solutions for improving their sustainability and smartness. This paper is focused on showing the evaluation results after the application of the evaluation framework to the energy actions in a district in the city of Valladolid (Spain).

Home electricity demand has increased uninterrupted and is expected in 2050 to doubles the demanded in 2010. Making reasonable use of electricity is increasingly important and, in that way, different policies are carried out based on knowledge of how it is used. This article presents a procedure for measuring the potential electricity consumption in Uruguay. The study takes as main axis the appliance ownership information revelled by a national survey about severe socioeconomic aspects, and combines it with data on the characteristics of appliances, collected from local shops with an internet presence. Based on this data, an index of potential electricity consumption is performed for different census areas. To validate the analysis, it uses electricity consumption data from the ECD-UY (Electricity Consumption Data set of UruguaY) dataset and performs OLS linear regressions to evaluate real consumption and index correlation. The implementation uses Jupyter notebooks, language Python version 3, and utils libraries such as Pandas and Numpy. Results indicate that the departments with the highest index score are located on the West/Southwest coastlines. About census sections and segments in Montevideo, results show that the highest score areas are located in the South/Southeast coastlines, while lowest score ones are located in the outskirts. The validation process was limited by the lack of real consumption data.

Energy demand management is an important technique for smart grids, under the paradigm of smart cities. Direct control of devices is useful for demand management, but it has the disadvantage of affecting user comfort. This article presents an approach for defining an index to estimate the discomfort associated with an active demand management consisting of the interruption of domestic electric water heaters to perform a load shifting. The index is defined based on estimations of water utilization and water temperature using continuous power consumption measurements of water heaters. A stochastic forecasting model is applied, including an Extra Trees Regressor and a linear model for water temperature. Monte Carlo simulations are performed to calculate the defined index. The evaluation of the proposed approach is performed using real data for both the forecasting model and the temperature model. The real effect of interruptions on the water temperature of two water heaters is compared to validate that the thermal discomfort index correctly models the impact on temperature. This result allows ordering devices by their thermal discomfort index and having a fair criterion to decide which ones should be interrupted.

Modeling, predicting, and forecasting ambient air pollution is an important way to deal with the degradation of the air quality in our cities because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven modeling, predicting, and forecasting outdoor pollution methods require an important amount of data, which may limit their accuracy. To deal with such a lack of data, we propose to train generative models, specifically conditional generative adversarial networks, to create synthetic nitrogen dioxide daily time series that will allow an unlimited generation of realistic data. The experimental results indicate that the proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.

Based in the current growth rate of metropolitan areas, providing infrastructures and services to allow the safe, quick and sustainable mobility of people and goods, is increasingly challenging. The European Union has been promoting diverse initiatives towards sustainable transport development and environment protection by setting targets for changes in the sector, as those proposed in the 2011 White Paper on transport. Under this context, this study aims at evaluating the environmental performance of the transport sector in the 28 European Union countries, from 2015 to 2017, towards the policy agenda established in strategic documents. The assessment of the transport environmental performance was made through the aggregation of seven sub-indicators into a composite indicator using a Data Envelopment Analysis approach. The model used to determine the weights to aggregate the sub-indicators is based on a variant of the Benefit of the Doubt model with virtual proportional weights restrictions. The results indicate that, overall, the European Union countries had almost no variation on its transport environmental performance during the time span under analysis. The inefficient countries can improve the transport sustainability mainly by drastically reducing the greenhouse gas emissions from fossil fuels combustion, increasing the share of freight transport that uses rail and waterways and also the share of transport energy from renewable sources.

This article presents a study of public transportation and accessibility to public services in Maldonado, Uruguay. Accessibility is a crucial concept in nowadays smart cities, to guarantee a proper mobility, citizen participation in social, economic, and cultural activities, and an overall good quality of life. Several data sources are studied and processed to characterize the accessibility provided by the public transportation system of Maldonado to public services, specifically to education centers. A matrix of travel times by public transportation is computed and used to define a flexible accessibility indicator to reach destinations of interest. Finally, an interactive visualization tool is developed to graphically display the computed information. The accessibility indicator constitutes an input for the decision-making of the transportation authorities of the studied area, as well as it allows identifying potential inequity situations.

Crowdsourcing is a phenomenon where groups of persons sometimes from different backgrounds participate to accomplish a task by making use of technology. Internet of Things (IoT) is able to incorporate a large number of heterogeneous devices such as sensors, surveillance cameras, smartphones, home appliances, etc., all data generated by these devices is processed and analysed to incorporate applications that will make life easier for the end users. This article proposes that community members of a specific urban zone, prone to flooding, collaborate in sharing information about weather conditions using IoT techniques. The gathered information is sent to a cloudlet to be analysed together with information from weather forecast and a network of sensors and surveillance cameras installed in specific areas inside and surrounding the studied zone. Having members of the very community studied involved in the process will exploit the available IoT technologies and the use of crowdsourcing at a lower cost leading to the development of what is called Smart City. This paper revises the available technology and proposes a system that will help in collecting and evaluating information for prediction purposes as to whether the community involved is at risk of being flooded. It is being noted that this risk is getting higher every year due to overpopulation, bad urbanisation, and climate change. Results show that the use of this technology will improve weather forecast so the community could react in time in case of flooding threats.

The reduction of CO₂ emissions in cities implies the generation of clean energy for the supply of the municipal energy demand. In the conversion to Smart Cities, the consumption sources and the generation possibilities should be considered as a whole, in such a way that all the urban elements can be integrated in the energy mix. In this study, bus shelters are evaluated as potential energy generators. The installation of PV panels with the optimal configuration can contribute to the supply of the energy needed in the bus shelter, but also to the generation of surplus energy. The analysis of the possibilities and the definition of the recommendable configuration of PV installations in bus shelters are performed using the city of Ávila (Spain) as case study. In this city, the PV generation reached with the optimal configuration (3500 kWh/year) can cover the energy demand of the bus shelters, including their role as lighting points in the city, and being able to contribute to other energy demands. For this study, geospatial information and a solar radiation model are incorporated in a Geospatial Information System (GIS) tool, specially developed to replicate this study in other cities.

This article presents a system developed for the collection and analysis of traffic data obtained from traffic camera videos using computational intelligence. The proposed system is developed using the modern object detection library Detectron2. A pipeline-type architecture is used for frame processing, where each step is an independent, configurable functional module, loosely coupled to the others. The validation of the proposed system is performed on real scenarios in Montevideo, Uruguay, under different conditions (daylight, nightlight, and different video qualities). Results demonstrate the effectiveness of the system in the considered scenarios.

This article presents the application of mathematical programming and evolutionary algorithms to solve a variant of the Bus Timetabling Synchronization Problem. A new problem model is proposed to include extended synchronization points, accounting for every pair of bus stops in a city, the transfer demands for each pair of lines, and the offset for lines in the considered scenario. Mixed Integer Programming and evolutionary algorithm are proposed to efficiently solve the problem. A relevant real case study is solved, for the public transportation system of Montevideo, Uruguay. Several scenarios are solved and results are compared with the no-synchronization solution and the current planning of such transportation system too. Experimental results indicate that the proposed approaches are able to significantly improve the current plannings. The Mixed Integer Programming algorithm computed the optimum solution for all scenarios, accounting for an improvement of up to 95% in successful synchronizations when compared with the actual timetable in Montevideo. The evolutionary algorithm is efficient too, improving up to 68% the synchronizations with respect to the current planning and systematically outperforming the baseline solutions. Waiting times for users are significantly improved too, up to 33% in tight problem instances.

This article presents an analysis of the current situation regarding sustainable mobility in Engineering Faculty, Universidad de la República, Montevideo, Uruguay. Sustainable mobility is a relevant issue in transportation within the novel paradigm of smart cities. The presented analysis is oriented to provide specific recommendations towards developing a sustainable mobility plan for Engineering Faculty and the surrounding neighborhood. The case study is analyzed considering the main concepts from related works and well-known quantitative and qualitative indicators. An empirical study based on questionnaires performed in the zone is introduced, providing interesting information for the study. The main results are discussed, including the motivations and issues that prevent users to move towards sustainable transportation modes. Specific suggestions are formulated to develop and improve sustainable mobility in the studied zone.

This study analyzes the impact of the COVID-19 pandemic on traffic congestion in 15 metropolitan areas of 13 Latin American countries. The database of the Traffic Congestion Intensity (TCI) of the IDB Invest Dashboard is used, it was developed from the alliance between the IDB and Waze and it is correlated with the contagions of the population published by Johns Hopkins Hospital University, for the period from March 9 to July 31, 2020, approximately five (5) months. For the analysis, the areas have been categorized into four (4) clusters, based on the Coefficient of Variation and the TCI/ WHO ratio. For each cluster, the graphs of the variation of the ΔTCI, the contagion cases, and the mobility recovery rate are analyzed. Among the conclusions include that the decrease in the number of infections and the flexibility of social distancing measures can be related to a recovery from congestion and that this can be measured as a function to the rate of recovery of mobility. In addition, the pandemic has revealed less collective and more agile forms of mobility, being this an important opportunity for the region to develop new forms of transport.

Technology enabled learning and communication are at the crossroads, which need flexible solutions. Flexible learning enables a learner to move seamlessly between the real and virtual world. We propose a novel flexible communication tool, “the smart book” to address these challenges. First, we review the role of the traditional book, its role in society today and the recent advances in augmented reality books. Next, we present a novel approach that integrates digital twins and mixed realities that is useful in communication, learning and for making decisions. We propose an initial vision of the architecture. Finally, we follow the Spiral of Creative Thought to create a first prototype with promising results. Our plan to further develop the architecture is to integrate this spiral with other software development methods. We need further iterations within the spiral to include final users and diverse applications.

This paper presents a methodology to convert a network section with generation sources, storage, and loads into an electrical microgrid. This conversion will allow greater autonomy and efficiency in its management. Besides, after the analysis of the recorded data, a reduction in the consumption of the distribution network can be achieved, and therefore, a reduction in the costs of the electricity bill. To achieve this transformation it is necessary to provide the network with intelligence, proposing a methodology based on four steps: identification and description of the elements that form it, choice of hardware and software for monitoring and controlling the system, establishment of communication between the different elements and creation of a control network framework for visualization. As a case study, the microgrid of the Renewable Energy Development Centre (CEDER) located in the province of Soria (Spain) is shown, formed by different sources of generation, storage systems, and consumption. All the elements of this microgrid are integrated with single free software, Home Assistant, installed in a Raspberry Pi 4 to provide the network with basic intelligence, control and monitoring in real-time through different communication protocols.

The paper introduces the Hyperloop inspired system by the company ZELEROS and provides a discussion about the alternatives for the acceleration. The ZELEROS system comprises a named propulsion system, based on an air turbine, and an acceleration system based on a linear motor. Different options have been discussed for the linear motor: induction machines, permanent magnet machines and switched reluctance machine, among some other hybrid solutions. Advantages and drawbacks are presented for all of them. For this particular application, where the length of the active side and the speed are really significant, the option of a linear switched reluctance motor results convenient both from the technical and economic points of view. A further discussion about the type of linear switched reluctance motor (LSRM) preferred is also accomplished during the paper, taking into account a particular model of the machine as well as its operation conditions regarding the frequency, dimensions and operation parameters. Three options are compared during the analysis: a single-side horizontal LSRM, a N-side vertical LSRM and a cylindrical LSRM. Qualitative and quantitative (based on the model) arguments are given in favour and against each alternative. Finally, the two-sided vertical switched reluctance machine has been considered as the most adequate for the development of the Zeleros Acceleration System.

The current collection of second-hand clothes containers that Cáritas has distributed throughout the Community of Madrid presents some inefficiencies. Through the remotely monitoring of these containers, this project intends to address and reduce them. To do this, a study of the different alternatives on the market is carried out and a prototype is developed to check the functionality of the system. The system is composed of three different parts: a hardware or device composed of an ultrasound sensor that sends the free capacity of the container through the communications network of Sigfox to its own back-end. By creating a callback this message is redirected to the Microsoft Azure cloud platform where it is processed, stored and displayed. In addition, the user can know the capacity of the containers through a web application or front-end and create optimized routes based on this information.

Soling and partial shading of solar panels are two of the most common conditions that affects the power yield of a photovoltaic (PV) installation. Even though human inspection can easily identify such situations, in the case of large power plants covering thousands of hectares it is not practical. In this regard, unmanned areal systems (UAS) represents a useful tool to gather images in a short time for the inspection of thousands of PV panels. Using RGB and infrared cameras, UAS can be used to perform visual inspection (VI) and infrared thermography (IRT) to detect failures in PV arrays. The present paper presents the results of an experiment designed to evaluate the effectiveness of VI and IRT for detecting soiling and partial shadowing. It has been found that for the aforementioned conditions VI are more effective. Also, the methodology presented can be used as a reference for future research for other techniques and other failures. The results provide technical-scientific information for those in charge of operation and maintenance to make an objective choice of failure detection techniques.