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A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption

Author

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  • Kyoungcheol Oh

    (Division of Architecture, College of Engineering, INHA University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea)

  • Eui-Jong Kim

    (Division of Architecture, College of Engineering, INHA University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea)

  • Chang-Young Park

    (Institute of Green Building and New Technology, Mirae Environment Plan Architects, Seoul 01905, Korea)

Abstract

Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environment and proposed a model that uses simulation results as the input for a data-driven model. Case studies were conducted with different quantities of data. The proposed hybrid method proposed in this study showed a higher prediction accuracy showing a cvRMSE of 22.8% and an MAE of 6.1% than using the conventional data-driven method and satisfying the tolerance criteria of ASHRAE Guideline 14 in all the test cases.

Suggested Citation

  • Kyoungcheol Oh & Eui-Jong Kim & Chang-Young Park, 2022. "A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9464-:d:878285
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    References listed on IDEAS

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    Cited by:

    1. Ma, Zhihao & Jiang, Gang & Hu, Yuqing & Chen, Jianli, 2025. "A review of physics-informed machine learning for building energy modeling," Applied Energy, Elsevier, vol. 381(C).

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