IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v292y2024ics0360544224002767.html
   My bibliography  Save this article

Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning

Author

Listed:
  • Cui, Can
  • Xue, Jing

Abstract

This paper proposes a novel optimal control method for multi-zone HVAC systems to enhance energy efficiency and improve occupant comfort. To address the disturbances in outdoor weather and indoor loads, the proposed method formulates the HVAC control problem as a dynamic Markov decision process, and employs deep reinforcement learning (DRL) techniques to obtain the optimal control policy. To manage multiple goals among thermal comfort, indoor air quality (IAQ) and system energy efficiency, a preference-inspired (P-ins) mechanism is designed to achieve the optimal balance among different objectives. The P-ins mechanism effectively guides the agent towards the optimal control policy with high convergence rate. The proposed method has been validated through EnergyPlus-Python co-simulation testbed with real-world data traces, and assessed by an overall evaluation indicator. Results demonstrate that the proposed method can reduce energy consumption without compromising thermal comfort and IAQ. Specifically, the occurrence of temperature violations is reduced below 0.8 %, and a maximum energy saving of 9.41 % can be achieved, compared with traditional methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002767
    DOI: 10.1016/j.energy.2024.130505
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224002767
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130505?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
    2. Kim, Wonuk & Jeon, Seung Won & Kim, Yongchan, 2016. "Model-based multi-objective optimal control of a VRF (variable refrigerant flow) combined system with DOAS (dedicated outdoor air system) using genetic algorithm under heating conditions," Energy, Elsevier, vol. 107(C), pages 196-204.
    3. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2023. "A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period," Applied Energy, Elsevier, vol. 334(C).
    4. Song, Kwonsik & Jang, Youjin & Park, Moonseo & Lee, Hyun-Soo & Ahn, Joseph, 2020. "Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings," Energy, Elsevier, vol. 206(C).
    5. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    6. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    7. Schibuola, Luigi & Scarpa, Massimiliano & Tambani, Chiara, 2018. "CO2 based ventilation control in energy retrofit: An experimental assessment," Energy, Elsevier, vol. 143(C), pages 606-614.
    8. Li, Chunxiao & Cui, Can & Li, Ming, 2023. "A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency," Applied Energy, Elsevier, vol. 329(C).
    9. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    10. Gao, Yixiang & Li, Shuhui & Fu, Xingang & Dong, Weizhen & Lu, Bing & Li, Zhongwen, 2020. "Energy management and demand response with intelligent learning for multi-thermal-zone buildings," Energy, Elsevier, vol. 210(C).
    11. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    12. Li, Wenzhuo & Tang, Rui & Wang, Shengwei & Zheng, Zhuang, 2023. "An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings," Applied Energy, Elsevier, vol. 349(C).
    13. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo, 2022. "Energy, economic, and environmental impacts of enhanced ventilation strategies on railway coaches to reduce Covid-19 contagion risks," Energy, Elsevier, vol. 256(C).
    14. 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).
    15. Kang, Dongju & Kang, Doeun & Hwangbo, Sumin & Niaz, Haider & Lee, Won Bo & Liu, J. Jay & Na, Jonggeol, 2023. "Optimal planning of hybrid energy storage systems using curtailed renewable energy through deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    16. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    17. Bie, Yiming & Liu, Yajun & Li, Shiwu & Wang, Linhong, 2022. "HVAC operation planning for electric bus trips based on chance-constrained programming," Energy, Elsevier, vol. 258(C).
    18. Ilbahar, Esra & Kahraman, Cengiz & Cebi, Selcuk, 2022. "Risk assessment of renewable energy investments: A modified failure mode and effect analysis based on prospect theory and intuitionistic fuzzy AHP," Energy, Elsevier, vol. 239(PA).
    19. Jia, Chunchun & Li, Kunang & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao, 2023. "Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm," Energy, Elsevier, vol. 283(C).
    20. Giampieri, A. & Ma, Z. & Ling-Chin, J. & Roskilly, A.P. & Smallbone, A.J., 2022. "An overview of solutions for airborne viral transmission reduction related to HVAC systems including liquid desiccant air-scrubbing," Energy, Elsevier, vol. 244(PA).
    21. Lavanya, R. & Murukesh, C. & Shanker, N.R., 2023. "Microclimatic HVAC system for nano painted rooms using PSO based occupancy regression controller," Energy, Elsevier, vol. 278(PA).
    22. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    23. Turanjanin, Valentina & Vučićević, Biljana & Jovanović, Marina & Mirkov, Nikola & Lazović, Ivan, 2014. "Indoor CO2 measurements in Serbian schools and ventilation rate calculation," Energy, Elsevier, vol. 77(C), pages 290-296.
    24. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
    25. Yang, Zheng & Ghahramani, Ali & Becerik-Gerber, Burcin, 2016. "Building occupancy diversity and HVAC (heating, ventilation, and air conditioning) system energy efficiency," Energy, Elsevier, vol. 109(C), pages 641-649.
    26. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
    27. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    28. de Araujo Passos, Luigi Antonio & Ceha, Thomas Joseph & Baldi, Simone & De Schutter, Bart, 2023. "Model predictive control of a thermal chimney and dynamic solar shades for an all-glass facades building," Energy, Elsevier, vol. 264(C).
    29. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
    30. Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
    31. Tang, Rui & Wang, Shengwei & Shan, Kui & Cheung, Howard, 2018. "Optimal control strategy of central air-conditioning systems of buildings at morning start period for enhanced energy efficiency and peak demand limiting," Energy, Elsevier, vol. 151(C), pages 771-781.
    32. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
    33. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    2. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    3. Cui, X. & Islam, M.R. & Chua, K.J., 2019. "Experimental study and energy saving potential analysis of a hybrid air treatment cooling system in tropical climates," Energy, Elsevier, vol. 172(C), pages 1016-1026.
    4. 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).
    5. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
    6. Nik, Vahid M. & Hosseini, Mohammad, 2023. "CIRLEM: a synergic integration of Collective Intelligence and Reinforcement learning in Energy Management for enhanced climate resilience and lightweight computation," Applied Energy, Elsevier, vol. 350(C).
    7. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    8. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2023. "A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period," Applied Energy, Elsevier, vol. 334(C).
    9. García Vázquez, C.A. & Cotfas, D.T. & González Santos, A.I. & Cotfas, P.A. & León Ávila, B.Y., 2024. "Reduction of electricity consumption in an AHU using mathematical modelling for controller tuning," Energy, Elsevier, vol. 293(C).
    10. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
    11. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    12. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
    13. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    14. Ayas Shaqour & Aya Hagishima, 2022. "Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types," Energies, MDPI, vol. 15(22), pages 1-27, November.
    15. Schibuola, Luigi & Scarpa, Massimiliano & Tambani, Chiara, 2018. "CO2 based ventilation control in energy retrofit: An experimental assessment," Energy, Elsevier, vol. 143(C), pages 606-614.
    16. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
    17. Gil-Baez, Maite & Barrios-Padura, Ángela & Molina-Huelva, Marta & Chacartegui, R., 2017. "Natural ventilation systems in 21st-century for near zero energy school buildings," Energy, Elsevier, vol. 137(C), pages 1186-1200.
    18. Wang, Zixuan & Xiao, Fu & Ran, Yi & Li, Yanxue & Xu, Yang, 2024. "Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 367(C).
    19. Lizana, Jesus & Serrano-Jimenez, Antonio & Ortiz, Carlos & Becerra, Jose A. & Chacartegui, Ricardo, 2018. "Energy assessment method towards low-carbon energy schools," Energy, Elsevier, vol. 159(C), pages 310-326.
    20. Sun, Fangyuan & Kong, Xiangyu & Wu, Jianzhong & Gao, Bixuan & Chen, Ke & Lu, Ning, 2022. "DSM pricing method based on A3C and LSTM under cloud-edge environment," Applied Energy, Elsevier, vol. 315(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002767. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.