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Building Heat Demand Prediction Based on Reinforcement Learning for Thermal Comfort Management

Author

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  • Chendong Wang

    (Tianjin Key Laboratory of Indoor Air Environmental Quality Control, Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

  • Lihong Zheng

    (Tianjin Key Laboratory of Indoor Air Environmental Quality Control, Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
    Tianjin Eco-City Green Building Research Institute, Tianjin 300467, China)

  • Jianjuan Yuan

    (School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Ke Huang

    (Tianjin Key Laboratory of Indoor Air Environmental Quality Control, Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
    Capital Construction Department, Tianjin University of Technology, Tianjin 300384, China)

  • Zhihua Zhou

    (Tianjin Key Laboratory of Indoor Air Environmental Quality Control, Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

Abstract

The accurate prediction of building heat demand plays the critical role in refined management of heating, which is the basis for on-demand heating operation. This paper proposed a prediction model framework for building heat demand based on reinforcement learning. The environment, reward function and agent of the model were established, and experiments were carried out to verify the effectiveness and advancement of the model. Through the building heat demand prediction, the model proposed in this study can dynamically control the indoor temperature within the acceptable interval (19–23 °C). Moreover, the experimental results showed that after the model reached the primary, intermediate and advanced targets in training, the proportion of time that the indoor temperature can be controlled within the target interval (20.5–21.5 °C) was over 35%, 55% and 70%, respectively. In addition to maintaining indoor temperature, the model proposed in this study also achieved on-demand heating operation. The model achieving the advanced target, which had the best indoor temperature control performance, only had a supply–demand error of 4.56%.

Suggested Citation

  • Chendong Wang & Lihong Zheng & Jianjuan Yuan & Ke Huang & Zhihua Zhou, 2022. "Building Heat Demand Prediction Based on Reinforcement Learning for Thermal Comfort Management," Energies, MDPI, vol. 15(21), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7856-:d:951041
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    References listed on IDEAS

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    1. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.

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