Short-term and seasonal photovoltaic power forecasting based on state-frequency memory recurrent network

With the rapid growth of the global renewable energy industry, photovoltaic (PV) power generation has become a crucial component of clean energy systems. However, PV power output exhibits significant variability and uncertainty due to weather conditions, seasonal shifts, and geographical differences. Developing an accurate and robust PV power prediction model is essential for improving the operational efficiency of PV plants, reducing power system regulation costs, and increasing the utilization rate of renewable energy. This study proposes a novel PV power forecasting model based on the State-Frequency Memory (SFM) recurrent network. The model incorporates a time-varying transformation mechanism for specific sequences, combined with discrete Fourier transform-based frequency decomposition and weight adjustment techniques, to effectively handle the frequency information in PV power output, thereby enhancing prediction accuracy. To evaluate the model’s effectiveness, historical datasets from the National Renewable Energy Laboratory in the United States were used for both short-term and seasonal PV forecasting. Comparative experiments with conventional short-term prediction models demonstrate that the proposed SFM-based model achieves superior performance, with the lowest root mean square error and mean absolute error values across various time scales and seasonal conditions. These results confirm the model’s high accuracy, robustness, and practical value in supporting reliable PV power forecasting.