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Automated machine learning-based framework of heating and cooling load prediction for quick residential building design
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China.
College of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, China.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.ORCID iD: 0000-0002-8704-8538
2023 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 274, article id 127334Article in journal (Refereed) Published
Abstract [en]

Reducing the heating and cooling load through energy-efficient building design can help decarbonize the building sector. Heating and cooling load prediction using machine learning (ML) techniques become increasingly important in the rapid assessment of building design variables at the early design stage. However, when applying the ML techniques, it still requires expert knowledge and manually frequent intervention to improve the prediction performance. Hence, this study proposed an automated machine learning (AutoML)-based framework to automatically generate the optimal ML pipelines for heating and cooling load prediction. An experimental dataset of residential buildings was used to evaluate the proposed framework. The proposed framework achieved the best performance with R2 of 0.9965 and RMSE of 0.602 kWh/m2 for heating load prediction, and R2 of 0.9899 and RMSE of 0.973 kWh/m2 for cooling load prediction. The prediction results showed that the proposed framework outperformed the other improved ML models from the representative studies in the last five years. Further, an explainable analysis of the ML models was explored to reveal the relationships between design variables and heating and cooling load. The proposed framework aims at promoting the AutoML-based framework to designers for building energy performance prediction without excessive ML knowledge and manually frequent intervention.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 274, article id 127334
Keywords [en]
Automated machine learning, Energy-efficient building, Heating and cooling load, Residential building design
National Category
Energy Engineering Energy Systems
Identifiers
URN: urn:nbn:se:umu:diva-206455DOI: 10.1016/j.energy.2023.127334ISI: 000966965100001Scopus ID: 2-s2.0-85151011404OAI: oai:DiVA.org:umu-206455DiVA, id: diva2:1749388
Available from: 2023-04-06 Created: 2023-04-06 Last updated: 2023-09-05Bibliographically approved

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Lu, ChujiePenaka, Santhan ReddyOlofsson, Thomas

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