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A model-based design optimization strategy for ground source heat pump systems with integrated photovoltaic thermal collectors

机译:具有集成光伏集热器的地源热泵系统的基于模型的设计优化策略

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摘要

This paper presents a model-based design optimization strategy for ground source heat pump systems with integrated solar photovoltaic thermal collectors (GSHP-PVT). A dimension reduction strategy using Morris global sensitivity analysis was first used to determine the key design parameters of the GSHP-PVT system. A model-based design optimization strategy was then formulated to identify the optimal values of the key design parameters to minimize the life-cycle cost (LCC) of the GSHP-PVT system, in which an artificial neural network (ANN) model was used for performance prediction and a genetic algorithm (GA) was implemented as the optimization technique. A simulation system of a GSHP-PVT system developed using TRNSYS was used to generate necessary performance data for dimension reduction analysis, and for the ANN model training and validation. The results showed that the ANN model used was able to provide an acceptable prediction of the operational cost of the GSHP-PVT system. In comparison to two baseline cases, the 20-year life cycle cost (LCC) of the GSHP-PVT system studied can be decreased by 20.1% and 10.2% respectively, when using the optimal values determined by the proposed optimization strategy. This design optimization strategy can be potentially adapted to formulate the design optimization strategies for GSHP systems and other building energy systems.
机译:本文提出了基于模型的集成太阳能光伏集热器(GSHP-PVT)的地源热泵系统的设计优化策略。首先使用Morris全局灵敏度分析的降维策略来确定GSHP-PVT系统的关键设计参数。然后制定了基于模型的设计优化策略,以识别关键设计参数的最佳值,以最小化GSHP-PVT系统的生命周期成本(LCC),其中使用了人工神经网络(ANN)模型性能预测和遗传算法(GA)被实现为优化技术。使用TRNSYS开发的GSHP-PVT系统的仿真系统用于生成必要的性能数据以进行降维分析以及ANN模型的训练和验证。结果表明,所使用的ANN模型能够为GSHP-PVT系统的运营成本提供可接受的预测。与两个基准案例相比,使用建议的优化策略确定的最佳值时,所研究的GSHP-PVT系统的20年生命周期成本(LCC)可以分别降低20.1%和10.2%。该设计优化策略可以潜在地适用于制定GSHP系统和其他建筑能源系统的设计优化策略。

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