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In recent years, deep learning-based solar forecasting using all-sky imageshas emerged as a promising approach for alleviating uncertainty in PV powergeneration. However, the stochastic nature of cloud movement remains a majorchallenge for accurate and reliable solar forecasting. With the recent advancesin generative artificial intelligence, the synthesis of visually plausible yetdiversified sky videos has potential for aiding in forecasts. In this study, weintroduce \emph{SkyGPT}, a physics-informed stochastic video prediction modelthat is able to generate multiple possible future images of the sky withdiverse cloud motion patterns, by using past sky image sequences as input.Extensive experiments and comparison with benchmark video prediction modelsdemonstrate the effectiveness of the proposed model in capturing cloud dynamicsand generating future sky images with high realism and diversity. Furthermore,we feed the generated future sky images from the video prediction models for15-minute-ahead probabilistic solar forecasting for a 30-kW roof-top PV system,and compare it with an end-to-end deep learning baseline model SUNSET and asmart persistence model. Better PV output prediction reliability and sharpnessis observed by using the predicted sky images generated with SkyGPT comparedwith other benchmark models, achieving a continuous ranked probability score(CRPS) of 2.81 (13\% better than SUNSET and 23\% better than smart persistence)and a Winkler score of 26.70 for the test set. Although an arbitrary number offutures can be generated from a historical sky image sequence, the resultssuggest that 10 future scenarios is a good choice that balances probabilisticsolar forecasting performance and computational cost.

SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT