A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modelling Energy Performance of Non-domestic Buildings
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
This chapter presents an energy performance prediction model for the UK non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic building energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provides a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space.
Modelling Energy Performance of Non-domestic Buildings
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
This chapter presents an energy performance prediction model for the UK non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic building energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provides a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space.
Modelling Energy Performance of Non-domestic Buildings
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
This chapter presents an energy performance prediction model for the UK non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic building energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provides a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space.
Modelling Energy Performance of Non-domestic Buildings
Green Energy,Technology
Seyedzadeh, Saleh (author) / Pour Rahimian, Farzad (author)
Data-Driven Modelling of Non-Domestic Buildings Energy Performance ; Chapter: 7 ; 111-133
2021-01-16
23 pages
Article/Chapter (Book)
Electronic Resource
English
Evaluating energy performance in non-domestic buildings: A review
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Evaluating energy performance in non-domestic buildings: A review
| Online Contents | 2016
Evaluating energy performance in non-domestic buildings: A review
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Energy rating of non-domestic buildings
| British Library Conference Proceedings | 1996