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

Zhe Tian, Chuang Ye, Jie Zhu, Jide Niu and Yakai Lu
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Zhe Tian: School of Environmental Science and Engineering, Tianjin University, 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
Yakai Lu: School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China

Energies, 2023, vol. 16, issue 7, 1-20

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.

Keywords: HVAC system; offline optimization; rule-based control; scenario reduction; clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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