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Coordinative energy efficiency improvement of buildings based on deep reinforcement learning

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Listed:
  • Chenguan Xu
  • Wenqing Li
  • Yao Rao
  • Bei Qi
  • Bin Yang
  • Zhongdong Wang

Abstract

Due to the uncertainty of user’s behaviour and other conditions, the design of energy efficiency improvement methods in buildings is challenging. In this paper, a building energy management method based on deep reinforcement learning is proposed, which solves the energy scheduling problem of buildings with renewable sources and energy storage system and minimises electricity costs while maintaining the user’s comfort. Different from model-based methods, the proposed DRL agent makes decisions only by observing the measurable information without considering the dynamic of the building environment. Simulations based on real data verify the effectiveness of the proposed method.

Suggested Citation

  • Chenguan Xu & Wenqing Li & Yao Rao & Bei Qi & Bin Yang & Zhongdong Wang, 2023. "Coordinative energy efficiency improvement of buildings based on deep reinforcement learning," Cyber-Physical Systems, Taylor & Francis Journals, vol. 9(3), pages 260-272, July.
    • Handle: RePEc:taf:tcybxx:v:9:y:2023:i:3:p:260-272
    DOI: 10.1080/23335777.2022.2066181
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