GA-BP neural network photovoltaic power generation short-term forecast based on MIV analysis

被引：0

作者：

Wang, Yingli ^{[1
]}

Tao, Shuai ^{[1
]}

Hou, Xiaoxiao ^{[1
]}

Qi, Hong ^{[2
]}

机构：

[1] School of Measurement and Communication, Harbin University of Science and Technology, Harbin,150080, China

[2] College of Energy Science and Engineering, Harbin Institute of Technology, Harbin,150000, China

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2020年 / 41卷 / 08期

关键词：

In view of the problem of low stability and precision of network prediction caused by too many input variables in the neural network forecast model of photovoltaic power generation; a GA-BP neural network short-term forecast method of photovoltaic power generation based on improved MIV(Mean Impact Value) algorithm is proposed. Combining the Spearman correlation coefficient significance test and the improved MIV analysis in which Euclidean distance is adopted to calculate the variation factor; this approach obtains the degree of external correlation and internal correlation between the input variables (meteorological factor) and the output variable (photovoltaic power generation capacity); singles out the input variable which has the greatest correlation degree with the output variable; and utilizes the optimized neural network to conduct short-term forecast on photovoltaic power generation. The experimental results show that the mean square error of the approach is reduced to 0.6450 from 3.7034 of BP and 1.8552 of GA-BP forecast networks to 0.6450; which implies greater stability and precision of the forecast network. © 2020; Solar Energy Periodical Office Co; Ltd. All right reserved;

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引用

页码：236 / 242

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