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A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems

Author

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  • Seppo Sierla

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland)

  • Heikki Ihasalo

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland)

  • Valeriy Vyatkin

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
    Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden
    International Research Laboratory of Computer Technologies, ITMO University, 197101 St. Petersburg, Russia)

Abstract

Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to optimize one or more targets, such as indoor air quality, energy consumption and energy cost. The agent receives feedback from the HVAC systems to quantify how well these targets have been achieved. The feedback is captured by a reward function designed by the developer of the reinforcement learning agent. A few reviews have focused on the reward aspect of reinforcement learning applications for HVAC. However, there is a lack of reviews that assess how the actions of the reinforcement learning agent have been formulated, and how this impacts the possibilities to achieve various optimization targets in single zone or multi-zone buildings. The aim of this review is to identify the action formulations in the literature and to assess how the choice of formulation impacts the level of abstraction at which the HVAC systems are considered. Our methodology involves a search string in the Web of Science database and a list of selection criteria applied to each article in the search results. For each selected article, a three-tier categorization of the selected articles has been performed. Firstly, the applicability of the approach to buildings with one or more zones is considered. Secondly, the articles are categorized by the type of action taken by the agent, such as a binary, discrete or continuous action. Thirdly, the articles are categorized by the aspects of the indoor environment being controlled, namely temperature, humidity or air quality. The main result of the review is this three-tier categorization that reveals the community’s emphasis on specific HVAC applications, as well as the readiness to interface the reinforcement learning solutions to HVAC systems. The article concludes with a discussion of trends in the field as well as challenges that require further research.

Suggested Citation

  • Seppo Sierla & Heikki Ihasalo & Valeriy Vyatkin, 2022. "A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems," Energies, MDPI, vol. 15(10), pages 1-25, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3526-:d:813404
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    References listed on IDEAS

    as
    1. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    2. Alberto Carotenuto & Francesca Ceglia & Elisa Marrasso & Maurizio Sasso & Laura Vanoli, 2021. "Exergoeconomic Optimization of Polymeric Heat Exchangers for Geothermal Direct Applications," Energies, MDPI, vol. 14(21), pages 1-20, October.
    3. Francesca Ceglia & Adriano Macaluso & Elisa Marrasso & Carlo Roselli & Laura Vanoli, 2020. "Energy, Environmental, and Economic Analyses of Geothermal Polygeneration System Using Dynamic Simulations," Energies, MDPI, vol. 13(18), pages 1-34, September.
    4. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Wang, Xuan & Wang, Rui & Jin, Ming & Shu, Gequn & Tian, Hua & Pan, Jiaying, 2020. "Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    6. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    7. Haji Hosseinloo, Ashkan & Ryzhov, Alexander & Bischi, Aldo & Ouerdane, Henni & Turitsyn, Konstantin & Dahleh, Munther A., 2020. "Data-driven control of micro-climate in buildings: An event-triggered reinforcement learning approach," Applied Energy, Elsevier, vol. 277(C).
    8. Lee, Zachary E. & Zhang, K. Max, 2021. "Generalized reinforcement learning for building control using Behavioral Cloning," Applied Energy, Elsevier, vol. 304(C).
    9. 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).
    10. Kazmi, Hussain & Mehmood, Fahad & Lodeweyckx, Stefan & Driesen, Johan, 2018. "Gigawatt-hour scale savings on a budget of zero: Deep reinforcement learning based optimal control of hot water systems," Energy, Elsevier, vol. 144(C), pages 159-168.
    11. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    12. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    13. Ting Hu & Zhikun Ding, 2021. "An Integrated Prediction Model for Building Energy Consumption: A Case Study," Springer Books, in: Gui Ye & Hongping Yuan & Jian Zuo (ed.), Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, pages 1655-1665, Springer.
    14. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    15. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
    16. 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.
    17. Yang, Lei & Nagy, Zoltan & Goffin, Philippe & Schlueter, Arno, 2015. "Reinforcement learning for optimal control of low exergy buildings," Applied Energy, Elsevier, vol. 156(C), pages 577-586.
    18. Lork, Clement & Li, Wen-Tai & Qin, Yan & Zhou, Yuren & Yuen, Chau & Tushar, Wayes & Saha, Tapan K., 2020. "An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management," Applied Energy, Elsevier, vol. 276(C).
    19. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    20. Ce Chi & Kaixuan Ji & Penglei Song & Avinab Marahatta & Shikui Zhang & Fa Zhang & Dehui Qiu & Zhiyong Liu, 2021. "Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-32, April.
    21. Davide Coraci & Silvio Brandi & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings," Energies, MDPI, vol. 14(4), pages 1-26, February.
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