Content area
As the participation rate of photovoltaic power generation in the entire power system continues to increase, accurate photovoltaic power prediction technology is crucial for optimizing power system scheduling. However, in recent years, the frequent occurrence of extreme weather events has significantly increased the difficulty of accurately predicting photovoltaic power generation. Regarding photovoltaic power prediction methods under extreme weather conditions, this study first reviews existing research on photovoltaic power prediction methods from three aspects: dust storms, heavy rain, and snowfall. Based on this review, a GWO-LSTM grey wolf optimization time series prediction model based on K-Means clustering is proposed. First, the K-Means clustering algorithm is used to classify weather types. Then, based on the weather classification results, the predictive performance of the GWO-LSTM grey wolf optimization time series model, random forest prediction model, BP neural network model, and LSTM model is compared. The prediction results show that the GWO-LSTM model achieves the highest prediction accuracy, with an accuracy of approximately 95% under four weather conditions. This provides effective data support for the safe and stable operation of new power systems with a high proportion of photovoltaic grid connection.