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Year-round operational optimization of HVAC systems using hierarchical deep reinforcement learning for enhancing indoor air quality and reducing energy consumption

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  • Liao, Chenxin
  • Miyata, Shohei
  • Qu, Ming
  • Akashi, Yasunori

Abstract

Balancing user comfort demands with energy consumption in heating, ventilation, and air conditioning (HVAC) systems is a critical challenge in the field of control optimization. Currently, deep reinforcement learning (DRL) algorithms have achieved some success in optimizing HVAC system control. However, training agents with existing algorithms becomes challenging, when dealing with the hybrid action control problem. This study proposes an HVAC system optimization control method based on hierarchical deep reinforcement learning (HDRL), which aims to minimize system energy consumption while ensuring indoor comfort and maintaining air quality. The proposed HDRL method employs a two-layer agent architecture, where the bottom agent is responsible for optimizing the frequency control of fans and pumps and the top agent flexibly selects the daily operating mode based on outdoor meteorological conditions. We designed a multi-objective reward function based on Gaussian distribution, hyperbolic tangent function, energy consumption normalization for bottom agents, and temperature-based reward function for the top agent to achieve the reliable training of the agents. Experimental results indicated that the HDRL method significantly improves indoor temperature and CO2 concentration control during the year-round operation of the HVAC system. Compared to conventional PI control, the temperature compliance rate is increased by 8.37 %, and the combined temperature and CO2 compliance rate is improved by 7.8 %, while achieving a 3.95 % reduction in energy consumption.

Suggested Citation

  • Liao, Chenxin & Miyata, Shohei & Qu, Ming & Akashi, Yasunori, 2025. "Year-round operational optimization of HVAC systems using hierarchical deep reinforcement learning for enhancing indoor air quality and reducing energy consumption," Applied Energy, Elsevier, vol. 390(C).
    • Handle: RePEc:eee:appene:v:390:y:2025:i:c:s030626192500546x
    DOI: 10.1016/j.apenergy.2025.125816
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    References listed on IDEAS

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    1. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    2. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    3. Lei, Yue & Zhan, Sicheng & Ono, Eikichi & Peng, Yuzhen & Zhang, Zhiang & Hasama, Takamasa & Chong, Adrian, 2022. "A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings," Applied Energy, Elsevier, vol. 324(C).
    4. Singh, A. & Syal, M. & Grady, S.C. & Korkmaz, S., 2010. "Effects of green buildings on employee health and productivity," American Journal of Public Health, American Public Health Association, vol. 100(9), pages 1665-1668.
    5. Wang, Hao & Chen, Xiwen & Vital, Natan & Duffy, Edward & Razi, Abolfazl, 2024. "Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 356(C).
    6. Nam, KiJeon & Heo, SungKu & Li, Qian & Loy-Benitez, Jorge & Kim, MinJeong & Park, DuckShin & Yoo, ChangKyoo, 2020. "A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions," Applied Energy, Elsevier, vol. 266(C).
    7. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "Energy saving and indoor temperature control for an office building using tube-based robust model predictive control," Applied Energy, Elsevier, vol. 341(C).
    8. Kong, Meng & Dong, Bing & Zhang, Rongpeng & O'Neill, Zheng, 2022. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study," Applied Energy, Elsevier, vol. 306(PA).
    9. Gao, Yuan & Hu, Zehuan & Shi, Shanrui & Chen, Wei-An & Liu, Mingzhe, 2024. "Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan," Applied Energy, Elsevier, vol. 359(C).
    10. Shi, Shanrui & Miyata, Shohei & Akashi, Yasunori, 2025. "Event-driven model-based optimal demand-controlled ventilation for multizone VAV systems: Enhancing energy efficiency and indoor environmental quality," Applied Energy, Elsevier, vol. 377(PD).
    11. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty," Applied Energy, Elsevier, vol. 371(C).
    12. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    13. 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.
    14. Gao, Yuan & Shi, Shanrui & Miyata, Shohei & Akashi, Yasunori, 2024. "Successful application of predictive information in deep reinforcement learning control: A case study based on an office building HVAC system," Energy, Elsevier, vol. 291(C).
    15. 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).
    16. Jayathissa, P. & Luzzatto, M. & Schmidli, J. & Hofer, J. & Nagy, Z. & Schlueter, A., 2017. "Optimising building net energy demand with dynamic BIPV shading," Applied Energy, Elsevier, vol. 202(C), pages 726-735.
    17. Liu, Mingzhe & Guo, Mingyue & Fu, Yangyang & O’Neill, Zheng & Gao, Yuan, 2024. "Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications," Applied Energy, Elsevier, vol. 372(C).
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