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Impacts of uncertain information delays on distributed real-time optimal controls for building HVAC systems deployed on IoT-enabled field control networks

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  • Su, Bing
  • Wang, Shengwei
  • Li, Wenzhuo

Abstract

Distributed optimal control deployed on field control networks is receiving increasing attention with the rapid development of the Internet of Things (IoT), including its applications in HVAC systems. Information delays refer to the time delays in information exchange between devices integrated in communication networks. They could affect the performance of distributed optimal control, but are rarely concerned in HVAC field. This study investigates and quantifies the impacts of information delays on the performance of distributed optimal control strategies for HVAC systems through theoretical analysis and case studies, including a typical central cooling plant and a typical multi-zone air-conditioning system. The uncertain information delays are modeled by a Markov chain according to the characteristics of networks. Their impacts are quantified by comparing the performance of the distributed optimal control strategies involving the information delays with ideal performance. Results show that information delays significantly affected the convergence rate and control accuracy of the distributed optimal control strategies. These delays can result in a difference in optimized cooling tower outlet water temperature of up to 0.6 K and a number of iterations of up to180 (about nine times than in ideal conditions). Test results indicate the necessity of considering the impacts of information delays when developing distributed optimal control strategies for HVAC systems. This necessity exists for both future IoT-enabled and current LAN-based field control networks.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s0306261921007868
    DOI: 10.1016/j.apenergy.2021.117383
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    References listed on IDEAS

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    1. 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).
    2. Angelia Nedić & Asuman Ozdaglar, 2010. "Convergence rate for consensus with delays," Journal of Global Optimization, Springer, vol. 47(3), pages 437-456, July.
    3. Michailidis, Iakovos T. & Schild, Thomas & Sangi, Roozbeh & Michailidis, Panagiotis & Korkas, Christos & Fütterer, Johannes & Müller, Dirk & Kosmatopoulos, Elias B., 2018. "Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study," Applied Energy, Elsevier, vol. 211(C), pages 113-125.
    4. Su, Bing & Wang, Shengwei, 2020. "An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks," Applied Energy, Elsevier, vol. 274(C).
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