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Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads

Luo, X. J.; Oyedele, Lukumon O.; Ajayi, Anuoluwapo O.; Akinade, Olugbenga O.

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Authors

Xiaojun Luo Xiaojun.Luo@uwe.ac.uk
Research Fellow - Evolutionary Computing and Optimisation

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence



Abstract

Buildings are one of the significant sources of energy consumption and greenhouse gas emission in urban areas all over the world. Lighting control and building integrated photovoltaic (BIPV) are two effective measures in reducing overall primary energy consumption and carbon emission during building operation. Due to the complex energy nature of the building, accurate day-ahead prediction of heating, cooling, lighting loads and BIPV electrical power production is essential in building energy management. Owing to the changing metrological conditions, diversity and complexity of buildings, building energy load demands and BIPV electrical power production is highly variable. This may lead to poor building energy management, extra primary energy consumption or thermal discomfort. In this study, three machine learning-based multi-objective prediction frameworks are proposed for simultaneous prediction of multiple energy loads. The three machine learning techniques are artificial neural network, support vector regression and long-short-term-memory neural network. Since heating, cooling, lighting loads and BIPV electrical power production share similar affecting factors, it is computational time saving to adopt the proposed multi-objective prediction framework to predict multiple building energy loads and BIPV power production. The ANN-based predictive model results in the smallest mean absolute percentage error while SVM-based one cost the shortest computation time.

Citation

Luo, X. J., Oyedele, L. O., Ajayi, A. O., & Akinade, O. O. (2020). Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads. Sustainable Cities and Society, 61, https://doi.org/10.1016/j.scs.2020.102283

Journal Article Type Article
Acceptance Date May 20, 2020
Online Publication Date Jun 30, 2020
Publication Date Oct 1, 2020
Deposit Date Jul 1, 2020
Publicly Available Date Jul 1, 2021
Journal Sustainable Cities and Society
Print ISSN 2210-6707
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 61
Article Number 102283
DOI https://doi.org/10.1016/j.scs.2020.102283
Keywords Renewable Energy, Sustainability and the Environment; Geography, Planning and Development; Civil and Structural Engineering; Transportation
Public URL https://uwe-repository.worktribe.com/output/6149759
Additional Information This article is maintained by: Elsevier; Article Title: Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads; Journal Title: Sustainable Cities and Society; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.scs.2020.102283; Content Type: article; Copyright: © 2020 Elsevier Ltd. All rights reserved.

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