Abstract
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.
Abstract (translated)
近年来，使用所有天空图像的深度学习太阳能预测已成为减轻太阳能电池板发电不确定性的有前途的方法。然而，云运动的随机性质仍然是准确可靠的太阳能预测的一个主要挑战。随着生成人工智能的最新进展，通过将具有不同云运动模式的多样化天空视频合成起来，有可能帮助预测。在本研究中，我们介绍了 \emph{SkyGPT} 一个基于物理知识的随机视频预测模型，能够通过使用过去天空图像序列作为输入生成多种具有不同云运动模式的未来的天空图像。进行了广泛的实验并与其他基准视频预测模型进行了比较，证明了该模型在捕获云动态并生成高真实感和多样性的未来天空图像方面的 effectiveness。此外，我们使用视频预测模型从生成的未来天空图像中为一个30千瓦屋顶太阳能电池板进行了15分钟的 probabilistic 太阳能预测，并与 end-to-end 深度学习基线模型 SunSET 和智能坚持模型进行了比较。与其他基准模型相比，使用 SkyGPT 生成的预测天空图像在太阳能电池板输出预测可靠性和清晰度方面表现更好，连续排名概率得分(CRPS)为2.81(比 SunSET 好13\%，比智能坚持模型好23\%)，测试集Winkler得分为26.70。虽然可以从历史天空图像序列中生成任意数量的未来的图像，但结果表明，考虑10个未来的场景是平衡 probabilistic 太阳能预测性能和计算成本的好选择。
URL
https://arxiv.org/abs/2306.11682
