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A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks

Author

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  • Qing Yin

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, 66 Xi Dazhi Street, Harbin 150006, China)

  • Chunmiao Han

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, 66 Xi Dazhi Street, Harbin 150006, China)

  • Ailin Li

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, 66 Xi Dazhi Street, Harbin 150006, China)

  • Xiao Liu

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China)

  • Ying Liu

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, 66 Xi Dazhi Street, Harbin 150006, China)

Abstract

Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance.

Suggested Citation

  • Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7805-:d:1473418
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    References listed on IDEAS

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