Short-term PV power prediction based on XLSTM model

In order to solve the problem of short-term power prediction of photovoltaic power generation, an innovative method based on enhanced Long Short-term memory network (XLSTM) is proposed in this paper. Photovoltaic power generation is affected by weather, light and other factors, showing significant volatility and uncertainty, and traditional forecasting methods are difficult to accurately capture its complex timing change law. XLSTM deeply improves the memory mechanism of traditional LSTM, greatly enhances the ability of the model to capture long-term dependencies and improves the training efficiency. The model is trained by historical PV power generation data and meteorological data and can accurately predict the PV power generation in the short term. The experimental results show that compared with traditional LSTM and other common prediction methods, XLSTM model performs better in both prediction accuracy and calculation performance, providing a solid and reliable basis for intelligent scheduling of photovoltaic power, and helping to improve the stability and efficiency of photovoltaic power system operation.