High-Fidelity Full-Sky Video Prediction for Photovoltaic Ramp Event Forecasting

TL;DR

This paper introduces a generative framework combining sky video prediction and PV output forecasting to improve ultra-short-term solar ramp event prediction, enhancing grid stability.

Contribution

It presents a novel integration of a sky video prediction model with PV forecasting, achieving state-of-the-art accuracy and interpretability in PV ramp event prediction.

Findings

Achieves up to 16-minute ahead PV ramp prediction at 1-minute resolution.

Improves Critical Success Index (CSI) for ramp detection by 10%.

Enhances video prediction quality with better structural, perceptual, and temporal metrics.

Abstract

Accurate ultra-short-term forecasting of photovoltaic (PV) ramp events is essential for maintaining grid stability in solar-integrated power systems, particularly under rapidly changing cloud conditions. This paper presents a generative forecasting framework that integrates a future sky video prediction model (PhyDiffNet) with a ramp aware PV output forecasting model (RaPVFormer). Based on the relatively slow yet chaotic dynamics of cloud motion, the system forecasts ramp events up to 16 minutes in advance at a 1-minute resolution by capturing fine-grained spatiotemporal cloud patterns and generating high-fidelity full-sky video frames. Interpretability is enhanced through attention visualization, highlighting cloud occlusion regions that significantly influence irradiance variability. Supported by extensive quantitative evaluation, the proposed framework demonstrates state-of-the-art performance in both full-sky video prediction and PV output forecasting. It delivers consistent improvements in structural, perceptual, and temporal video quality, along with a 10% increase in Critical Success Index (CSI) for PV ramp detection. These results demonstrate the capability of AI driven multimodal sensing for ultra short term solar forecasting, supporting more reliable renewable integration and potentially reducing dependence on reserve capacity.