Ultra-Short-Term Prediction Method of Photovoltaic Power Generation Based on Improved GRNN-LSTM Combination Model

Ultra-short-term prediction of photovoltaic power generation is the prerequisite for real-time dispatch of the power system, in order to further improve the accuracy of ultra-short-term prediction of photovoltaic power, this paper proposes an ultra-short-term prediction method of photovoltaic power based on an improved Generalized Regression Neural Network(GRNN)-Long Short-Term Memory (LSTM) combination model. Firstly, in the GRNN model, an improved whale optimization algorithm based on grid constraints, quasi-reverse learning and local perturbations is proposed to solve the optimal smoothing factor of the model and improve the prediction accuracy. Secondly, in the LSTM model, a CNN convolutional layer and attention mechanism are introduced to better exploit temporal information and enhance the predictive capabilities for complex weather changes. Finally, a combination model threshold setting method based on an improved Dynamic Time Warping (DTW) algorithm is proposed. This method dynamically selects the best model's prediction results based on thresholds during different time periods, fully leveraging the advantages of each model to meet the diverse weather conditions in photovoltaic power prediction. Experimental results demonstrate that the improved GRNN model excels in predicting sunny weather power, the improved LSTM model excels in predicting complex weather power, and the combination model performs well across various meteorological conditions, providing a robust solution for photovoltaic power ultra-short-term prediction problems.