Solar power harbors immense potential in mitigating climate change by
substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent
variability of solar irradiance poses a significant challenge for seamlessly
integrating solar power into the electrical grid. While the majority of prior
research has centered on employing purely time series-based methodologies for
solar forecasting, only a limited number of studies have taken into account
factors such as cloud cover or the surrounding physical context. In this paper,
we put forth a deep learning architecture designed to harness spatio-temporal
context using satellite data, to attain highly accurate \textit{day-ahead}
time-series forecasting for any given station, with a particular emphasis on
forecasting Global Horizontal Irradiance (GHI). We also suggest a methodology
to extract a distribution for each time step prediction, which can serve as a
very valuable measure of uncertainty attached to the forecast. When evaluating
models, we propose a testing scheme in which we separate particularly difficult
examples from easy ones, in order to capture the model performances in crucial
situations, which in the case of this study are the days suffering from varying
cloudy conditions. Furthermore, we present a new multi-modal dataset gathering
satellite imagery over a large zone and time series for solar irradiance and
other related physical variables from multiple geographically diverse solar
stations. Our approach exhibits robust performance in solar irradiance
forecasting, including zero-shot generalization tests at unobserved solar
stations, and holds great promise in promoting the effective integration of
solar power into the grid.

本文采用深度学习，设计了一个利用卫星数据的时空上下文来高精度预测全球水平辐照度（GHI）的天际时间序列预测模型，并提出了一种预测不确定性分布的方法。作者通过将特别困难的日子（在这项研究中特别是经历变化多端的多云天）与简单的日子分开来评估模型性能，提出了一种测试方案。同时，作者还提出了一个新的多模态数据集，收集了来自多个地理位置不同的太阳能站的太阳能辐射和其他相关物理变量的卫星图像和时间序列。该方法在太阳辐照度预测中表现出鲁棒性，包括在未观测到的太阳能站进行零样本推广测试，并极有潜力促进太阳能与电网的有效整合。