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.
|Title of host publication||Data-Driven Modelling of Non-Domestic Buildings Energy Performance|
|Subtitle of host publication||Supporting Building Retrofit Planning|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||23|
|Publication status||Published - 16 Jan 2021|
|Name||Green Energy and Technology|
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