输入参数对空气式光伏热系统性能预测的影响研究

Zhonghua Zhao, Li Zhu, Yiping Wang, Qunwu Huang, Yong Sun
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引用次数: 0

摘要

通过收集PV/T(光伏热系统)的热量可以获得足够的热量,并且可以成功地避免因过热而导致的PV电池效率下降。风冷和液冷是两种常见的太阳能光伏/T冷却方案。风冷PV/T系统变量众多,传热数学模型比较复杂。利用BP (Back Propagation)神经网络方法可以建立仿真预测模型,对太阳能光伏/T系统的热电性能进行仿真和预测。仿真基于采暖季的单日实验数据,记录了BP神经网络在不同输入参数下(2组- 4组- 6组)的6种温差预测和电功率预测,得到了适当的训练水平。利用这些BP神经网络可以计算PV/T系统的电效率和热效率。通过对比2天的实验数据和BP神经网络模型的预测结果,BP神经网络模型的预测值与实际值的拟合程度满足要求。在两个预测日,电功率值的预测精度R2分别为0.97009和0.95538。温差数值拟合程度略差于电功率值。预测精度R2分别为0.90114和0.93547。本研究将有助于进一步预测和分析光伏/T系统在不同区域和气候条件下的应用效益,为光伏/T系统在建筑一体化中的应用提供有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Influence of Input Parameters by Back Propagation Neural Network on Performance Prediction of Air-type Photovoltaic Thermal System
Enough heat may be obtained by collecting the heat from the PV/T (photovoltaic thermal system), and the degradation of PV cell efficiency caused by overheating can be successfully avoided. Air cooling and liquid cooling are two common solar PV/T cooling solutions. The air-cooled PV/T system has numerous variables, and the heat transfer mathematical model is quite complicated. The BP (Back Propagation) neural network method can be used to create a simulation prediction model, which can be used to simulate and predict the thermal and electrical performance of a solar PV/T system. The simulation is based on data from a single-day experiment conducted during the heating season, and it records the BP neural network's six types of temperature difference prediction and electric power prediction under various input parameters (groups 2-group 4-group 6). The appropriate level of training has been attained. The electrical and thermal efficiency of PV/T systems can be calculated using these BP neural networks. The fitting degree of the anticipated value and the actual value of the BP neural network model fulfils the requirements when compared to the experimental data gathered over two days and the prediction results of the BP neural network model. On the two projected days, the prediction accuracy R2 of electric power value were 0.97009 and 0.95538, respectively. The temperature difference numerical fitting degree is somewhat worse than the electric power value. The R2 values were 0.90114 and 0.93547, respectively, for forecast accuracy. This research will assist in further predicting and analyzing the application benefit of PV/T systems in different regions and climate conditions, and will provide valuable information for PV/T system application in building integration.
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