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

Chendong Wang, Lihong Zheng, Jianjuan Yuan, Ke Huang and Zhihua Zhou ()
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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
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
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

Energies, 2022, vol. 15, issue 21, 1-20

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%.

Keywords: reinforcement learning; heat demand prediction; on-demand heating operation; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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