SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky
  Videos from Physics-constrained VideoGPT
Yuhao Nie Eric Zelikman Andea Scott Quentin Paletta Adam Brandt
In recent years, deep learning-based solar forecasting using all-sky images
has emerged as a promising approach for alleviating uncertainty in PV power
generation. However, the stochastic nature of cloud movement remains a major
challenge for accurate and reliable solar forecasting. With the recent advances
in generative artificial intelligence, the synthesis of visually plausible yet
diversified sky videos has potential for aiding in forecasts. In this study, we
introduce \emph{SkyGPT}, a physics-informed stochastic video prediction model
that is able to generate multiple possible future images of the sky with
diverse cloud motion patterns, by using past sky image sequences as input.
Extensive experiments and comparison with benchmark video prediction models
demonstrate the effectiveness of the proposed model in capturing cloud dynamics
and generating future sky images with high realism and diversity. Furthermore,
we feed the generated future sky images from the video prediction models for
15-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 a
smart persistence model. Better PV output prediction reliability and sharpness
is observed by using the predicted sky images generated with SkyGPT compared
with 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 of
futures can be generated from a historical sky image sequence, the results
suggest that 10 future scenarios is a good choice that balances probabilistic
solar forecasting performance and computational cost.