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

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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. Guixiang Xue & Yu Pan & Tao Lin & Jiancai Song & Chengying Qi & Zhipan Wang, 2019. "District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model," Energies, MDPI, vol. 12(11), pages 1-21, June.
    2. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    3. Zheng, Xinye & Wei, Chu & Qin, Ping & Guo, Jin & Yu, Yihua & Song, Feng & Chen, Zhanming, 2014. "Characteristics of residential energy consumption in China: Findings from a household survey," Energy Policy, Elsevier, vol. 75(C), pages 126-135.
    4. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    5. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    6. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    7. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    8. Yuan, Jianjuan & Wang, Chendong & Zhou, Zhihua, 2019. "Study on refined control and prediction model of district heating station based on support vector machine," Energy, Elsevier, vol. 189(C).
    9. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    10. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei & Zhou, Zhihua, 2022. "Evaluation of the operation data for improving the prediction accuracy of heating parameters in heating substation," Energy, Elsevier, vol. 238(PB).
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