Skill‐Driven Data Sampling and Deep Learning Framework for Minute‐Scale Solar Forecasting with Sky Images

Accurate very short‐term solar irradiance forecasting is crucial for optimizing the integration of solar energy into power systems. Herein, an image‐based deep learning framework for minute‐scale solar irradiance prediction is presented. The locally developed model is benchmarked against two commercial forecasting solutions deployed at the same experimental site, demonstrating superior accuracy and adaptability. A key contribution is the introduction of a skill‐driven sampling algorithm based on clear sky index persistence error, which optimizes the training dataset by excluding low‐utility samples while retaining essential physical features like solar zenith and azimuth angles. This algorithm enables the exclusion of up to 30% of the original training data, resulting in ≈16% savings in computational resources without affecting forecast accuracy validated using a test set of 324 991 observations. The model achieves a skill score of 7.63%, significantly outperforming the commercial models, which exhibit negative skill scores under the same conditions.