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Accelerating Optimal Control Strategy Generation for HVAC Systems Using a Scenario Reduction Method: A Case Study

Author

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  • Zhe Tian

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Tianjin Key Laboratory of Building Environment and Energy, Tianjin 300072, China)

  • Chuang Ye

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Jie Zhu

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Jide Niu

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Tianjin Key Laboratory of Building Environment and Energy, Tianjin 300072, China)

  • Yakai Lu

    (School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China)

Abstract

Learning an optimal control strategy from the optimized operating dataset is a feasible way to improve the operational efficiency of HVAC systems. The operation dataset is the key to ensuring the global optimality and universality of the operation strategy. Currently, the model-based method is commonly used to generate datasets that cover all operating scenarios throughout the cooling season. However, thousands of iterative optimizations of the model also lead to high computational costs. Therefore, this paper proposed a scenario reduction method in which similar operating scenarios were grouped into clusters to significantly reduce the number of optimization calculations. First, k-means clustering (with dry-bulb temperature, wet-bulb temperature, and cooling load as features) was used to select typical scenarios from operating scenarios for the entire cooling season. Second, the model-based optimization was performed with the typical scenarios to generate the optimal operating dataset. Taking a railway station in Beijing as a case study, the results show that the optimization time for the typical scenarios was only 1.4 days, which was reduced by 93.1% compared with the 20.6 days required to optimize the complete cooling season scenario. The optimal control rules were extracted, respectively, from the above datasets generated under the two schemes, and the results show that the deviation of energy saving rate was only 0.45%. This study shows that the scenario reduction method can significantly speed up the generation of the optimal control strategy dataset while ensuring the energy-saving effect.

Suggested Citation

  • Zhe Tian & Chuang Ye & Jie Zhu & Jide Niu & Yakai Lu, 2023. "Accelerating Optimal Control Strategy Generation for HVAC Systems Using a Scenario Reduction Method: A Case Study," Energies, MDPI, vol. 16(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:2988-:d:1106880
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    References listed on IDEAS

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    1. Jiale Tang & Kuixing Liu & Weijie You & Xinyu Zhang & Tuomi Zhang, 2023. "Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining," Energies, MDPI, vol. 16(14), pages 1-19, July.
    2. Hyang-A Park & Gilsung Byeon & Wanbin Son & Jongyul Kim & Sungshin Kim, 2023. "Data-Driven Modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin," Energies, MDPI, vol. 16(20), pages 1-14, October.

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