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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Energies

سال: 2023

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en16072988