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.

本研究提出了一种名为 SkyGPT 的物理学指导下的随机视频预测模型，能够产生多个可能的未来图像，并且通过将其应用于太阳能预测中，能够实现更好的太阳能输出预测可靠性和锐度。

SkyGPT：使用受物理约束的 VideoGPT 合成的天空视频进行概率短期太阳能预测

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

Solar forecasting from ground-based sky images has shown great promise in
reducing the uncertainty in solar power generation. With more and more sky
image datasets open sourced in recent years, the development of accurate and
reliable deep learning-based solar forecasting methods has seen a huge growth
in potential. In this study, we explore three different training strategies for
solar forecasting models by leveraging three heterogeneous datasets collected
globally with different climate patterns. Specifically, we compare the
performance of local models trained individually based on single datasets and
global models trained jointly based on the fusion of multiple datasets, and
further examine the knowledge transfer from pre-trained solar forecasting
models to a new dataset of interest. The results suggest that the local models
work well when deployed locally, but significant errors are observed when
applied offsite. The global model can adapt well to individual locations at the
cost of a potential increase in training efforts. Pre-training models on a
large and diversified source dataset and transferring to a target dataset
generally achieves superior performance over the other two strategies. With 80%
less training data, it can achieve comparable performance as the local baseline
trained using the entire dataset.

本文利用三个异构数据集探索了太阳能预测模型的三种不同训练策略，并比较了局部模型和全局模型的性能，结果表明预训练模型在少量训练数据下可以优于其他两种策略。