Accurate photovoltaic (PV) power forecasting is essential for grid operation but remains difficult due to nonlinear multi-scale dynamics and seasonal distribution shifts. This work presents MKAN-iTransformer, a cascaded framework that integrates two existing components—the Multi-Scale Kolmogorov–Arnold Network (MKAN) for scale-aware temporal representation learning and iTransformer for variable-wise attention and inter-variable dependency modeling—under a 15-minute single-step setting. Experiments on a real-world 30 MW PV plant dataset from the Chinese State Grid Renewable Energy Generation Forecasting Competition use chronological splits within each season. MKAN-iTransformer achieves the best overall performance in spring, autumn, and winter. In spring, it reaches MSE=2. 892, RMSE=1. 701, MAE=0. 864, and R^{2}=0. 947, improving over LSTM by 23. 5%/12. 5%/20. 5% (MSE/RMSE/MAE). In autumn, it attains MSE=2. 884, RMSE=1. 698, MAE=0. 774, and R^{2}=0. 962, reducing errors vs. iTransformer by 16. 5%/8. 7%/12. 4%. In winter, it achieves MSE=1. 721, RMSE=1. 312, MAE=0. 443, and R^{2}=0. 969, yielding 81. 6%/57. 1%/71. 9% error reductions vs. Transformer. Ablation further confirms the complementarity between MKAN and iTransformer and shows that direct KAN integration can be unstable under winter shifts (KAN-iTransformer: MSE=7. 082, R^{2}=0. 872).
Liu et al. (Tue,) studied this question.