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Automated machine learning-based framework of heating and cooling load prediction for quick residential building design

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  • Lu, Chujie
  • Li, Sihui
  • Reddy Penaka, Santhan
  • Olofsson, Thomas

Abstract

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.

Suggested Citation

  • Lu, Chujie & Li, Sihui & Reddy Penaka, Santhan & Olofsson, Thomas, 2023. "Automated machine learning-based framework of heating and cooling load prediction for quick residential building design," Energy, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223007284
    DOI: 10.1016/j.energy.2023.127334
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    References listed on IDEAS

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    Cited by:

    1. Adam Slowik & Dorin Moldovan, 2024. "Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction," Energies, MDPI, vol. 17(12), pages 1-23, June.
    2. Yan, Xiuying & Ji, Xingxing & Meng, Qinglong & Sun, Hang & Lei, Yu, 2024. "A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism," Energy, Elsevier, vol. 292(C).
    3. Kang, Yiting & Zhang, Dongjie & Cui, Yu & Xu, Wei & Lu, Shilei & Wu, Jianlin & Hu, Yiqun, 2024. "Integrated passive design method optimized for carbon emissions, economics, and thermal comfort of zero-carbon buildings," Energy, Elsevier, vol. 295(C).
    4. Wang, Guimei & Moayedi, Hossein & Thi, Quynh T. & Mirzaei, Mojtaba, 2024. "Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms," Energy, Elsevier, vol. 302(C).

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