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A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use

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  • Li, Wenzhuo
  • Wang, Shengwei

Abstract

A trade-off problem exists in ventilation systems to ensure acceptable indoor air quality (IAQ) with minimized energy use. It is often solved by the centralized optimization approach today. However, the dynamic operation conditions of ventilation systems and the changing expectations of users make the centralized optimal control not flexible and effective in responding to those dynamics and changes. Meanwhile, the distributed installation layouts of sensing and control networks provide appropriate application platforms for distributed optimal control. This paper therefore proposes a multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering IAQ and energy use by optimizing ventilation air volumes of individual rooms and primary air-handling unit (PAU). This distributed approach decomposes the complex optimization problem into a number of simple optimization problems. Distributed agents, corresponding to individual rooms and the PAU, are assigned to handle these decomposed problems. A central coordinating agent coordinates these agents to find the optimal solutions. Two control test cases under different outdoor weather conditions are conducted on a TRNSYS-MATLAB co-simulation testbed to validate the proposed multi-agent based distributed approach for optimal control by comparing with a baseline control approach and a centralized optimal control approach. Results of the distributed approach can provide almost the same outputs as the expected optimum given by the centralized optimal control approach. The experiences of implementing the proposed distributed approach show its effectiveness in solving complex optimization problems and optimizing multi-zone ventilation systems as well as good scalability and reconfigurability.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:275:y:2020:i:c:s0306261920308837
    DOI: 10.1016/j.apenergy.2020.115371
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    6. 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).
    7. Li, Wenzhuo & Wang, Shengwei & Koo, Choongwan, 2021. "A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method," Applied Energy, Elsevier, vol. 287(C).
    8. 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).
    9. Sinha, Anshuman & Thakkar, Harshul & Rezaei, Fateme & Kawajiri, Yoshiaki & Realff, Matthew J., 2022. "Reduced building energy consumption by combined indoor CO2 and H2O composition control," Applied Energy, Elsevier, vol. 322(C).
    10. Giuseppe Anastasi & Carlo Bartoli & Paolo Conti & Emanuele Crisostomi & Alessandro Franco & Sergio Saponara & Daniele Testi & Dimitri Thomopulos & Carlo Vallati, 2021. "Optimized Energy and Air Quality Management of Shared Smart Buildings in the COVID-19 Scenario," Energies, MDPI, vol. 14(8), pages 1-17, April.
    11. Su, Wei & Ai, Zhengtao & Liu, Jing & Yang, Bin & Wang, Faming, 2023. "Maintaining an acceptable indoor air quality of spaces by intentional natural ventilation or intermittent mechanical ventilation with minimum energy use," Applied Energy, Elsevier, vol. 348(C).
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    13. Zhang, Sheng & Ai, Zhengtao & Lin, Zhang, 2021. "Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving," Applied Energy, Elsevier, vol. 293(C).
    14. Wan, Taocheng & Bai, Yan & Wang, Tingxiang & Wei, Zhuo, 2022. "BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures," Applied Energy, Elsevier, vol. 313(C).
    15. Jiang, Zixin & Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk," Applied Energy, Elsevier, vol. 334(C).
    16. 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).
    17. Pouranian, Fatemeh & Akbari, Habibollah & Hosseinalipour, S.M., 2021. "Performance assessment of solar chimney coupled with earth-to-air heat exchanger: A passive alternative for an indoor swimming pool ventilation in hot-arid climate," Applied Energy, Elsevier, vol. 299(C).
    18. Su, Bing & Wang, Shengwei & Li, Wenzhuo, 2021. "Impacts of uncertain information delays on distributed real-time optimal controls for building HVAC systems deployed on IoT-enabled field control networks," Applied Energy, Elsevier, vol. 300(C).

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