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Erschienen in:

2021 | OriginalPaper | Buchkapitel

1. Introduction

verfasst von : Saleh Seyedzadeh, Farzad Pour Rahimian

Erschienen in: Data-Driven Modelling of Non-Domestic Buildings Energy Performance

Verlag: Springer International Publishing

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Abstract

In the UK, buildings are responsible for 46% of all carbon dioxide (\(CO_2\)) emissions [20]. This figure is 40% in the USA and 27% in Australia [15]. Accordingly, the enhancement of energy efficiency of buildings has become an essential matter in order to reduce the amount of gas emission as well as fossil fuel consumption. An annual saving of 60 billion Euro is estimated as a result of the improvement of EU buildings energy performance by 20% [21].

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Nächstes Kapitel Building Energy Performance Assessment Methods

Asadi E, da Silva MG, Antunes CH, Dias L (2012) A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Build Environ 56:370–378. https://doi.org/10.1016/j.buildenv.2012.04.005CrossRef

Asadi E, da Silva MG, Antunes CH, Dias L (2013) State of the art on retrofit strategies selection using multi-objective optimization and genetic algorithms. In: Nearly zero energy building refurbishment. Springer, Berlin, pp 279–297. https://doi.org/10.1007/978-1-4471-5523-2_11

Asadi E, Silva MGD, Antunes CH, Dias L, Glicksman L (2014) Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application. Energy Build 81:444–456. https://doi.org/10.1016/j.enbuild.2014.06.009CrossRef

Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP (2016a) Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality. Appl Energy 174:37–68. https://doi.org/10.1016/j.apenergy.2016.04.078CrossRef

Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP (2016b) Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort. Energy Build 111:131–144. https://doi.org/10.1016/j.enbuild.2015.11.033CrossRef

Ascione F, Bianco N, Mauro GM, Vanoli GP (2019) A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin. Appl Energy 241:331–361. https://doi.org/10.1016/j.apenergy.2019.03.028CrossRef

Bell M (2004) Energy efficiency in existing buildings: the role of building regulations. In: Proceedings of Construction and Building Research (COBRA) conference, September, pp 7–8

Bre F, Fachinotti VD (2017) A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings. Energy Build 154:283–294. https://doi.org/10.1016/j.enbuild.2017.08.002CrossRef

Committee on Climate Change (2013) Meeting carbon budgets: progress report on climate change, chapter 3: progress reducing emissions from buildings. Technical report, Committee on climate change. https://www.theccc.org.uk/wp-content/uploads/2013/06/CCC-Prog-Rep_Chap3_singles_web_1.pdf

10.

Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Huang YJ, Pedersen CO, Strand RK, Liesen RJ, Fisher DE, Witte MJ, Glazer J (2001) EnergyPlus: creating a new-generation building energy simulation program. Energy Build 33(4):319–331. https://doi.org/10.1016/S0378-7788(00)00114-6CrossRef

11.

Delay T, Farmer S, Jennings T (2009) Building the future, today: transforming the economic and carbon performance of the buildings we work in. Technical report, carbon trust. http://www.carbontrust.com/media/77252/ctc765_building_the_future__today.pdf

12.

Delgarm N, Sajadi B, Delgarm S, Kowsary F (2016) A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: case study in Iran. Energy Build 127:552–560. https://doi.org/10.1016/j.enbuild.2016.05.052CrossRef

13.

Diakaki C, Grigoroudis E, Kabelis N, Kolokotsa D, Kalaitzakis K, Stavrakakis G (2010) A multi-objective decision model for the improvement of energy efficiency in buildings. Energy 35(12):5483–5496. https://doi.org/10.1016/j.energy.2010.05.012CrossRef

14.

Ferrara M, Rolfo A, Prunotto F, Fabrizio E (2019) EDeSSOpt—Energy Demand and Supply Simultaneous Optimization for cost-optimized design: application to a multi-family building. Appl Energy 236:1231–1248. https://doi.org/10.1016/j.apenergy.2018.12.043CrossRef

15.

Filippin C (2000) Benchmarking the energy efficiency and greenhouse gases emissions of school buildings in central Argentina. Build Environ 35(5):407–414. https://doi.org/10.1016/S0360-1323(99)00035-9CrossRef

16.

García Kerdan I, Raslan R, Ruyssevelt P (2016) An exergy-based multi-objective optimisation model for energy retrofit strategies in non-domestic buildings. Energy 117:506–522. https://doi.org/10.1016/j.energy.2016.06.041CrossRef

17.

Ibn-Mohammed T, Greenough R, Taylor S, Ozawa-Meida L, Acquaye A (2014) Integrating economic considerations with operational and embodied emissions into a decision support system for the optimal ranking of building retrofit options. Build Environ 72:82–101. https://doi.org/10.1016/j.buildenv.2013.10.018CrossRef

18.

Jafari A, Valentin V (2017) An optimization framework for building energy retrofits decision-making. Build Environ 115:118–129. https://doi.org/10.1016/j.buildenv.2017.01.020CrossRef

19.

Jensen OM, Hansen AR, Kragh J (2016) Market response to the public display of energy performance rating at property sales. Energy Policy 93:229–235. https://doi.org/10.1016/j.enpol.2016.02.029CrossRef

20.

Kelly S, Crawford-Brown D, Pollitt MG (2012) Building performance evaluation and certification in the UK: is SAP fit for purpose? Renew Sustain Energy Rev 16(9):6861–6878. https://doi.org/10.1016/j.rser.2012.07.018CrossRef

21.

Li X, Bowers CP, Schnier T (2010) Classification of energy consumption in buildings with outlier detection. IEEE Trans Ind Electron 57(11):3639–3644. https://doi.org/10.1109/TIE.2009.2027926CrossRef

22.

Malatji EM, Zhang J, Xia X (2013) A multiple objective optimisation model for building energy efficiency investment decision. Energy Build 61:81–87. https://doi.org/10.1016/j.enbuild.2013.01.042CrossRef

23.

Ministry of Housing CLG (2020) Live tables on energy performance of buildings certificates. Technical report. http://bit.ly/3bP98lb

24.

Pountney C (2012) Better carbon saving: using a genetic algorithm to optimise building carbon reductions. In: Proceedings of the 2012 Building Simulation and Optimization Conference, September, pp 165–172. http://www.ibpsa.org/proceedings/BSO2012/3A1.pdf

25.

Si J (2017) Green retrofit of existing non-domestic buildings as a multi criteria decision making process. PhD thesis. https://doi.org/10.1016/j.scs.2016.06.013

26.

The Energy Systems Research Unit (ESRU) (2011) ESP-r. http://www.esru.strath.ac.uk/Programs/ESP-r.htm

27.

TRNSYS17 (2015) A transient systems simulation program. http://sel.me.wisc.edu/trnsys/

28.

UK Department of Energy & Climate (2014) Private rented sector minimum energy efficiency standard regulations (Non-Domestic, England and Wales ). Technical Report July, Department of Energy and Climate Change (DECC), London. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/401378/Non_Dom_PRS_Energy_Efficiency_Regulations_-_Gov_Response__FINAL_1_1__04_02_15_.pdf

Titel: Introduction
verfasst von: Saleh Seyedzadeh
Farzad Pour Rahimian
Verlag: Springer International Publishing
Buch: Data-Driven Modelling of Non-Domestic Buildings Energy Performance
Print ISBN: 978-3-030-64750-6

Electronic ISBN: 978-3-030-64751-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-64751-3_1

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