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Published in:
2021 | OriginalPaper | Chapter
7. Modelling Energy Performance of Non-domestic Buildings
Authors : Saleh Seyedzadeh, Farzad Pour Rahimian
Published in: Data-Driven Modelling of Non-Domestic Buildings Energy Performance
Publisher: Springer International Publishing
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Abstract
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.