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.

翻译：电站输出功率的提前预测对电网稳定性及确保不间断供电至关重要。然而，由于自然能源的混沌行为，可再生能源的预测面临巨大挑战。本文提出了一种从天空图像估算短期太阳辐照度的新方法。所提算法从天空图像中提取特征，并利用基于学习的技术来估算太阳辐照度。通过两个公开的天空图像数据集对所提机器学习算法的性能进行了评估。这些数据集包含2004年至2020年16年间超过35万张图像，并以每张图像对应的全球水平辐照度作为基准真值。与文献中提出的现有计算复杂度较高的最先进算法相比，我们的方法在即时预测和提前4小时的短期预测中，以更低的计算复杂度取得了具有竞争力的结果。