Ultra-Short-Term Photovoltaic Ramp Forecasting via Physics-Guided Generative Sky Video Prediction

Abstract

Minute-scale photovoltaic (PV) ramps caused by rapidly evolving cloud occlusion pose challenges to short-timescale power balancing and require reliable advance warning. This paper develops an event-oriented ultra-short-term PV forecasting framework that predicts future cloud evolution as an intermediate representation linking sky observations to PV output. PhyDiffNet combines physics-informed cloud-motion modeling with generative video prediction, while RaPVFormer integrates observed and predicted sky states, sun-location information, and historical PV measurements to forecast PV trajectories and ramp events up to 16 minutes ahead. Evaluation on the SKIPP'D dataset shows that the proposed framework achieves a Critical Success Index of 96.2\% for ramps beginning 1--4 minutes ahead and retains 84.9\% for those beginning 13--16 minutes ahead, outperforming all deployable baselines across the evaluated lead-time intervals. At a validation-calibrated 95\% reliability target, it reduces the required fast upward reserve margin by 34.2\% relative to the Persistence Model and by 11.6\% relative to the strongest deployable baseline under the adopted reserve metric. These results show that explicit future-cloud prediction improves advance warning of PV ramps and translates forecasting gains into lower short-timescale balancing margins under a common reliability target.