Research on power generation prediction of photovoltaic power station based on improved neural network
DOI:
https://doi.org/10.4108/ew.11917Keywords:
PV power forecast, FO algorithm, GA-BP algorithmAbstract
The large-scale construction of photovoltaic power plants makes more and more green power connected and utilized, which also brings great impact on the carrying capacity of the power grid. Forecasting the power generation of photovoltaic power plants can better promote the stable regulation of the power grid and promote the consumption of new energy. In this paper, the influence of main environmental factors on photovoltaic power generation is analyzed by Pearson similarity, and a photovoltaic prediction model is established. The model is solved by combining adaptive genetic algorithm and improved Drosophila algorithm to optimize neural network. Finally, the historical sample data of photovoltaic power plants are used for simulation. By comparing the photovoltaic power generation prediction effects of single BP algorithm, GA-BP combined algorithm and GA-FOA-BP intelligent algorithm, it is proved that the method of combining improved Drosophila algorithm to optimize neural network and genetic algorithm to solve the prediction model can reduce the instability of photovoltaic output prediction to some extent and has good application value.
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Copyright (c) 2026 Yuan Hu, Qin Xie, Zhao Liu, Mengjin Hu, Hongwei Li, Bo Yuan, Yaping Wang
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