Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.

中文翻译：

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基于Sky Imager的机器学习对太阳辐射的预测

提前预测发电厂的输出功率对于电网的稳定性和确保不间断的服务至关重要。但是，由于自然能源的混乱行为，很难预测可再生能源。本文提出了一种从天空图像估算短期太阳辐射的新方法。所提出的算法从天空图像中提取特征，并使用基于学习的技术来估计太阳辐照度。拟议的机器学习（ML）算法的性能是使用两个可公开获取的天空图像数据集进行评估的。从2004年到2020年，数据集包含超过350,000张图像，间隔时间为16年，从2004年到2020年，每张图像的相应全球水平辐照度（GHI）作为基本事实。