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Risk-based online robust optimal control of air-conditioning systems for buildings requiring strict humidity control considering measurement uncertainties

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  • Zhuang, Chaoqun
  • Wang, Shengwei

Abstract

The total floor area and energy consumption of buildings or spaces requiring strict temperature and humidity control have been growing rapidly worldwide. A major challenge for achieving energy-efficient control of air-conditioning systems in such applications is the measurement uncertainties underlying the systems’ online optimal control decisions under ever-changing working conditions. This paper proposes a risk-based online robust optimal control strategy for multi-zone air-conditioning systems considering component performance degradation and measurement uncertainties. A risk-based online control decision scheme, as the core of the strategy, is developed for decision-making by compromising the failure risks and energy benefits of different control modes considering uncertainties in the information used. The proposed strategy is tested and implemented in a simulation platform based on an existing pharmaceutical industrial building. The results show that the proposed strategy made the optimal online control decisions, allowing for the measurement uncertainties. Compared with a commonly used control strategy, the proposed strategy achieved approximately 20% overall energy saving in the test period.

Suggested Citation

  • Zhuang, Chaoqun & Wang, Shengwei, 2020. "Risk-based online robust optimal control of air-conditioning systems for buildings requiring strict humidity control considering measurement uncertainties," Applied Energy, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919321397
    DOI: 10.1016/j.apenergy.2019.114451
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    References listed on IDEAS

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    1. Shan, Kui & Wang, Shengwei, 2017. "Energy efficient design and control of cleanroom environment control systems in subtropical regions – A comparative analysis and on-site validation," Applied Energy, Elsevier, vol. 204(C), pages 582-595.
    2. Min, Yunran & Chen, Yi & Yang, Hongxing, 2019. "A statistical modeling approach on the performance prediction of indirect evaporative cooling energy recovery systems," Applied Energy, Elsevier, vol. 255(C).
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    4. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Adaptive full-range decoupled ventilation strategy and air-conditioning systems for cleanrooms and buildings requiring strict humidity control and their performance evaluation," Energy, Elsevier, vol. 168(C), pages 883-896.
    5. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Probabilistic optimal design of cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
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    Citations

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    Cited by:

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    3. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
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    5. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).

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