Data driven model improved by multi-objective optimisation for prediction of building energy loads

Seyedzadeh, Saleh, Rahimian, Farzad Pour, Oliver, Stephen, Glesk, Ivan and Kumar, Bimal (2020) Data driven model improved by multi-objective optimisation for prediction of building energy loads. Automation in Construction, 116. p. 103188. ISSN 0926-5805

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Abstract

Machine learning (ML) has been recognised as a powerful method for modelling building energy consumption. The capability of ML to provide a fast and accurate prediction of energy loads makes it an ideal tool for decision making tasks related to sustainable design and retrofit planning. However, the accuracy of these ML models is dependent on the selection of the right hyper-parameters for a specific building dataset. This paper proposes a method for optimising ML models for forecasting both heating and cooling loads. The technique employs multi-objective optimisation with evolutionary algorithms to search the space of possible parameters. The proposed approach not only tunes single model to precisely predict building energy loads but also accelerates the process of model optimisation. The study utilises simulated building energy data generated in EnergyPlus to validate the proposed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies.

Item Type:	Article
Uncontrolled Keywords:	Building energy loads, Building energy prediction, Machine learning, Model optimisation, Energy performance
Subjects:	G400 Computer Science H200 Civil Engineering K200 Building
Department:	Faculties > Engineering and Environment > Architecture and Built Environment
Depositing User:	John Coen
Date Deposited:	03 Nov 2020 10:21
Last Modified:	30 Nov 2020 16:01
URI:	http://nrl.northumbria.ac.uk/id/eprint/44666

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