IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v334y2023ics0306261922018797.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922018797
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.120622?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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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).
    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).
    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. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    2. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
    3. 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).
    4. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    5. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    6. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    7. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    8. 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).
    9. Zeng, Lanting & Qiu, Dawei & Sun, Mingyang, 2022. "Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks," Applied Energy, Elsevier, vol. 324(C).
    10. 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).
    11. 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).
    12. 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.
    13. Qiu, Dawei & Dong, Zihang & Zhang, Xi & Wang, Yi & Strbac, Goran, 2022. "Safe reinforcement learning for real-time automatic control in a smart energy-hub," Applied Energy, Elsevier, vol. 309(C).
    14. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    15. Esmaeilzadeh, Ahmad & Deal, Brian & Yousefi-Koma, Aghil & Zakerzadeh, Mohammad Reza, 2023. "How combination of control methods and renewable energies leads a large commercial building to a zero-emission zone – A case study in U.S," Energy, Elsevier, vol. 263(PD).
    16. Zhang, Xiongfeng & Lu, Renzhi & Jiang, Junhui & Hong, Seung Ho & Song, Won Seok, 2021. "Testbed implementation of reinforcement learning-based demand response energy management system," Applied Energy, Elsevier, vol. 297(C).
    17. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    18. Raman, Naren Srivaths & Chen, Bo & Barooah, Prabir, 2022. "On energy-efficient HVAC operation with Model Predictive Control: A multiple climate zone study," Applied Energy, Elsevier, vol. 324(C).
    19. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    20. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.

    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:appene:v:334:y:2023:i:c:s0306261922018797. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.