Machine learning modelling for predicting non-domestic buildings energy performance : a model to support deep energy retrofit decision-making

Seyedzadeh, Saleh and Pour Rahimian, Farzad and Oliver, Stephen and Rodriguez, Sergio and Glesk, Ivan (2020) Machine learning modelling for predicting non-domestic buildings energy performance : a model to support deep energy retrofit decision-making. Applied Energy, 279. 115908. ISSN 0306-2619

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    Abstract

    Non-domestic buildings contribute 20% of the UK's annual carbon emissions. A contribution exacerbated by its ageing stock of which only 7% is considered new-build. Consequently, the government has set regulations to decrease the amount of energy take-up by buildings which currently favour deep energy retrofitting analysis for decision-making and demonstrating compliance. Due to the size and complexity of non-domestic buildings, identifying optimal retrofit packages can be very challenging. The need for effective decision-making has led to the wide adoption of artificial intelligence in the retrofit strategy design process. However, the vast retrofit solution space and high time-complexity of energy simulations inhibit artificial intelligence's application. This paper presents an energy performance prediction model for 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 buildings 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 provide a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space. The model is evaluated by assessing three thousand retrofit variations of a case study building, achieving a root mean square error of 1.02 kgCO 2∕m 2×year equal to 1.7% of error.