Prediction of Photovoltaic Power Generation Based on NARX Neural Network-Wavelet Decomposition

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Prediction of Photovoltaic Power Generation Based on NARX Neural Network-Wavelet Decomposition

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[1]SHI Ruxin,WANG Deshun,YU Tao,et al.Prediction of Photovoltaic Power Generation Based on NARX Neural Network-Wavelet Decomposition[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):79-84.[doi:10.13705/j.issn.1671-6833.2020.06.015]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 41卷 Number of periods: 2020 06 Page number: 79-84 Column: Public date: 2020-12-31

Title:: Prediction of Photovoltaic Power Generation Based on NARX Neural Network-Wavelet Decomposition

Author(s):: SHI Ruxin1; WANG Deshun2; YU Tao1; XUE Jinhua2; FENG Xinzhen2; DOU Chunxia3; 1.Changzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd.,Changzhou 213000, China; 2.Nanjing Branch,China Electric Power Research Institute,Nanjing 210009,China; 3.Institude of Advanced Technology,Nanjing University of Posts and Telecommunications, Nanjing 210009,China

Keywords:: prediction of photovoltaic power; wavelet decomposition; NARX neural network; wavelet reconstruction; BP neural network

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2020.06.015

Abstract:: Aiming to reduce large forecast error in current photovoltaic power generation forecast, based on wavelet decomposition-NARX neural network combined prediction method was proposed. The historical PV sequence was decomposed into high frequency and low frequency components by wavelet decomposition, and the high and low frequency data were used as NARX neural network input, and PV output power was used as neural network output to train and obtain the predicted output. Then wavelet reconstruction was used to derive PV power generation prediction data. The simulation results showed that prediction error of new combined prediction algorithm was 1.47% lower than that of traditional BP neural network, and new prediction algorithm could reduce running time by nearly 5 s.

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