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A delay-tolerant distributed optimal control method concerning uncertain information delays in IoT-enabled field control networks of building automation systems

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  • Su, Bing
  • Wang, Shengwei

Abstract

Distributed optimal control deployed on field control networks has gotten increasing attention with the rapid development and wide applications of the Internet of Things, including the applications in building automation. Information delays, time delays in information exchange between different devices integrated in communication networks, can affect the performance of distributed optimal control but have rarely received attention in the building automation and HVAC (heating, ventilation, and air conditioning) fields. This paper proposes a delay-tolerant control method to reduce the impacts of uncertain information delays on the performance of the distributed optimal control of HVAC systems. The proposed method reduces the impacts of information delays through synchronizing the local optimization results used for convergence determination and adaptively setting the step-size used for updating Lagrange multiplier. The purpose of synchronizing local optimization results is to reduce the impacts of information delays on accuracy of the optimization results. The purpose of setting the step-size adaptively is to reduce the impacts of information delays on the convergence rate. The computational load of the proposed method is 40 FLOPs (floating-point operations), which can be handled by typical smart sensors. Test results show that the proposed delay-tolerant control method could effectively reduce the impacts of information delays on optimization accuracy and convergence rate, thereby improving the energy performance of the distributed optimal control strategy under conditions where delays occur.

Suggested Citation

  • Su, Bing & Wang, Shengwei, 2021. "A delay-tolerant distributed optimal control method concerning uncertain information delays in IoT-enabled field control networks of building automation systems," Applied Energy, Elsevier, vol. 301(C).
  • Handle: RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008989
    DOI: 10.1016/j.apenergy.2021.117516
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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. Wang, Zhu & Wang, Lingfeng & Dounis, Anastasios I. & Yang, Rui, 2012. "Multi-agent control system with information fusion based comfort model for smart buildings," Applied Energy, Elsevier, vol. 99(C), pages 247-254.
    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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