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Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system

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  • Jiang, Yuliang
  • Zhu, Shanying
  • Xu, Qimin
  • Yang, Bo
  • Guan, Xinping

Abstract

In this paper, we study multi-zone air environment comfort and building energy conversation for the heating, ventilation, and air conditioning (HVAC) system. Each zone is equipped with the air-handling unit (AHU) and fan coil unit to regulate the supply air state for comfortable zone climate. It is known that the multi-zone air environment appears spatial and temporal variation due to the hygro-thermal interaction which is an extremely complex nonlinear dynamic process. In this study, hybrid models incorporating the first-principles model with full form dynamic linearization (FFDL)-based data-driven model have been proposed to precisely describe the multi-zone climate dynamics including unknown nonlinearity and uncertainty. Then model-free adaptive control (MFAC) scheme is designed for multi-zone climate control, which contains controller design, parameter estimation and reset mechanism to ensure system control performance. Finally, different case studies are conducted to test the performance of model prediction and system control for the multi-zone climate environment. Model validation shows that the zone climate prediction of the proposed hybrid model shows better agreement with actual measured data. The step response results display that the hybrid model-based MFAC can realize multi-zone climate control performance improvement in faster convergence without stable bias. By comparing with prior work, the hybrid model-based MFAC has the potential to get closer to actual situation in cost-effective multi-zone climate control.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261922018797
    DOI: 10.1016/j.apenergy.2022.120622
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    References listed on IDEAS

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    1. 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).
    2. Mei, Jun & Xia, Xiaohua, 2017. "Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 195(C), pages 439-452.
    3. Mei, Jun & Xia, Xiaohua & Song, Mengjie, 2018. "An autonomous hierarchical control for improving indoor comfort and energy efficiency of a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 221(C), pages 450-463.
    4. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    5. Li, Wenzhuo & Wang, Shengwei, 2022. "A fully distributed optimal control approach for multi-zone dedicated outdoor air systems to be implemented in IoT-enabled building automation networks," Applied Energy, Elsevier, vol. 308(C).
    6. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    7. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    8. 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).
    9. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
    10. Jiang, Yuliang & Wang, Xinli & Zhao, Hongxia & Wang, Lei & Yin, Xiaohong & Jia, Lei, 2020. "Dynamic modeling and economic model predictive control of a liquid desiccant air conditioning," Applied Energy, Elsevier, vol. 259(C).
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    Cited by:

    1. 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).

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