Recurrent Fourier-Kolmogorov Arnold Networks for photovoltaic power forecasting

Accurate day-ahead forecasting of photovoltaic (PV) power generation is crucial for power system scheduling. To overcome the inaccuracies and inefficiencies of current PV power generation forecasting models, this paper introduces the Recurrent Fourier-Kolmogorov Arnold Network (RFKAN). Initially, recurrent kernel nodes are employed to investigate the interdependencies within sequences. Subsequently, Fourier series are applied to extract periodic features, enhancing forecasting accuracy and training speed. Ablation studies conducted using data from a PV power plant in Tieling City, Liaoning Province, validate the effectiveness of these two structural enhancements. Comparative experiments with baseline and state-of-the-art models further underscore the efficiency of RFKAN. The results indicate that RFKAN achieves the best forecasting performance with a grid depth of 100 and an input sequence length of 2, reducing RMSE and MAE by at least 5%, increasing CORR by 2%, and decreasing training time by 24% compared to advanced models.