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A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems

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  • Blad, C.
  • Bøgh, S.
  • Kallesøe, C.
  • Raftery, Paul

Abstract

This paper presents a laboratory study of Offline-trained Reinforcement Learning (RL) control of a Heating Ventilation and Air-Conditioning (HVAC) system. We conducted the experiments on a radiant floor heating system consisting of two temperature zones located in Denmark. The buildings are subjected to real-world weather. A previous paper describes the algorithm we tested, which we summarize in this paper. First, we present a benchmarking test which we conducted during spring 2021 and winter 2021/2022. This data is used in the Offline RL framework to train and deploy the RL policy, which we then tested during winter 2021/2022 and spring 2022. An analysis of the data shows that the RL policy showed predictive control-like behavior, and reduced the oscillations of the system by a minimum of 40%. Additionally, we show that the RL policy is minimum 14% more cost-effective than the traditional control policy used in the benchmarking test.

Suggested Citation

  • Blad, C. & Bøgh, S. & Kallesøe, C. & Raftery, Paul, 2023. "A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems," Applied Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:appene:v:337:y:2023:i:c:s030626192300171x
    DOI: 10.1016/j.apenergy.2023.120807
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    References listed on IDEAS

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    1. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    2. Blad, Christian & Bøgh, Simon & Kallesøe, Carsten Skovmose, 2022. "Data-driven Offline Reinforcement Learning for HVAC-systems," Energy, Elsevier, vol. 261(PB).
    3. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    4. Kuldeep Kurte & Jeffrey Munk & Olivera Kotevska & Kadir Amasyali & Robert Smith & Evan McKee & Yan Du & Borui Cui & Teja Kuruganti & Helia Zandi, 2020. "Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    5. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    6. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    7. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
    8. Christian Blad & Simon Bøgh & Carsten Kallesøe, 2021. "A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems," Energies, MDPI, vol. 14(22), pages 1-20, November.
    9. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
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    1. Silvestri, Alberto & Coraci, Davide & Brandi, Silvio & Capozzoli, Alfonso & Borkowski, Esther & Köhler, Johannes & Wu, Duan & Zeilinger, Melanie N. & Schlueter, Arno, 2024. "Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control," Applied Energy, Elsevier, vol. 368(C).

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