Research on Short-Term Prediction Method of Photovoltaic Power Generation Based on Improved Snake Optimization Algorithm for Optimizing Gate Recurrent Unit

In order to further improve the current accuracy of photovoltaic power generation prediction and improve the shortcomings of low prediction accuracy of traditional gate recurrent cycle units. A short-term prediction model for photovoltaic power generation based on Principal Component Analysis(PCA)-Improved Snake Optimizer (ISO)-Gate Recurrent Unit (GRU) is proposed. Firstly, PCA is used to extract key factors of feature sequences, reduce interference from unrelated original sequences, and reduce model dimensions. Then, an improved snake optimization algorithm is used to optimize the parameters of the GRU network, Using the GRU prediction network to model different variable feature sequences, the prediction of photovoltaic power generation is achieved. Finally, through simulation of a photovoltaic power station in Tianjin, the results show that the proposed prediction model has higher prediction accuracy and better prediction effect compared to other prediction models under four weather types.