文章基本信息

标题：Short-term PV/T module temperature prediction based on PCA-RBF neural network
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作者：Jiyong Li ; Zhendong Zhao ; Yisheng Li 等
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：121
期号：5
页码：052045
DOI：10.1088/1755-1315/121/5/052045
语种：English
出版社：IOP Publishing
摘要：Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.