Global issues pertaining to climate change have necessitated the rapid deployment of new energy sources, such as photovoltaic (PV) generation. In smart grids, accurate forecasting is essential to ensure the reliability and economy of the power system. However, PV generation is severely affected by meteorological factors, which hinders accurate forecasting. Various types of data, such as local measurement data, numerical weather prediction, and satellite images, can reflect meteorological dynamics over different time scales. This paper proposes a novel data-driven forecasting framework based on deep learning, which integrates an advanced U-net and an encoder-decoder architecture to cooperatively process multi-source (time series recording and satellite image) data. The adaption of the neural networks to the data sources and the collaborative learning of both spatial and temporal features boost the model accuracy. The experimental results for 50 real-world PV power stations indicate that the proposed framework features a higher accuracy than that of other baseline models.

中文翻译：

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基于多源数据和深度学习方法的小时内光伏发电预测

与气候变化有关的全球问题要求快速部署新能源，例如光伏 (PV) 发电。在智能电网中，准确的预测对于确保电力系统的可靠性和经济性至关重要。然而，光伏发电受气象因素影响较大，难以准确预测。各种类型的数据，如本地测量数据、数值天气预报和卫星图像，可以反映不同时间尺度上的气象动态。本文提出了一种基于深度学习的新型数据驱动预测框架，该框架集成了先进的 U-net 和编码器-解码器架构，以协同处理多源（时间序列记录和卫星图像）数据。神经网络对数据源的适应以及空间和时间特征的协同学习提高了模型的准确性。50 个真实世界光伏电站的实验结果表明，所提出的框架具有比其他基线模型更高的精度。