云计算
计算机科学
稳健性(进化)
深度学习
云量
太阳能
可再生能源
太阳能
人工智能
机器学习
间歇性
气象学
功率(物理)
工程类
生物化学
化学
物理
湍流
量子力学
电气工程
基因
操作系统
作者
Quentin Paletta,Guillermo Terrén-Serrano,Yunlong Nie,Binghui Li,Jacob Bieker,Wenqi Zhang,Laurent Dubus,Soumyabrata Dev,Cong Feng
标识
DOI:10.1016/j.adapen.2023.100150
摘要
Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.
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