Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A well-established approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply variability to adapt the response of the grid. In solar energy, short-term changes in electricity production caused by occluding clouds can be predicted at different time scales from all-sky cameras (up to 30-min ahead) and satellite observations (up to 6h ahead). In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting. Both deterministic and probabilistic predictions are evaluated in different weather conditions (clear-sky, cloudy, overcast) and with different input configurations (sky images, satellite observations and/or past irradiance values). Our results show that the hybrid model benefits predictions in clear-sky conditions and improves longer-term forecasting. This study lays the groundwork for future novel approaches of combining sky images and satellite observations in a single learning framework to advance solar nowcasting.
翻译:将间歇性可再生能源大规模纳入电网具有挑战性。旨在解决这一困难的既定办法包括预测即将出现的能源供应变异性以适应电网的反应。在太阳能方面,从全天空照相机(前方30分钟以下)和卫星观测(前方6小时以下)的不同时间尺度,可以预测阴云云云层造成的电力生产短期变化。在本研究中,我们将云层覆盖的这两个互补观点纳入一个单一的机器学习框架,以改进(前方60分钟以下)的辐照性预报。确定性和概率性预测都是在不同天气条件下(清空、云层、超载)和不同的输入配置(天空图像、卫星观测和/或过去的辐照值)下进行评估的。我们的结果表明,混合模型有利于在清空条件下预测并改进较长期的预报。这一研究为今后将天空图像和卫星观测结合在一个单一学习框架内以推进太阳现在的预测提供了基础。