Incremental Learning for Fast Shading Adaption in Solar Power Forecasting

This study presents a novel hybrid incremental learning approach for short-term solar power prediction under dynamic shading conditions, using time window-based incremental learning to adapt a pre-trained prediction model. The proposed approach rapidly adapts to changes in shading patterns without the need for full model retraining. The model is configured to use an estimation period, after which it requires three additional days to effectively compensate for shading effects. The results show that the proposed method significantly reduces forecast errors, improving the RMSE by up to 19.27% and the MAE by up to 31.81% in strong shading scenarios compared to transfer learning approaches. Moreover, the method requires only one tenth of the computation time. The proposed method provides a scalable, robust, and efficient solution for solar power forecasting, particularly in scenarios with frequent and strong shading.