Developing solar power generation technology is an efficient approach to relieving the global environmental crisis. However, solar energy is an energy source with strong uncertainty, which restricts large-scale photovoltaic (PV) applications until accurate solar energy predictions can be achieved. PV power forecasting methods have been widely researched based on existing predictions of satellite-derived solar irradiance, whereas modeling cloud motion directly from satellite images is still a tough task. In this study, an end-to-end short-term forecasting model is proposed to take satellite images as inputs, and it can learn the cloud motion characteristics from stacked optical flow maps. In order to reduce the huge size of measurements, static regions of interest (ROIs) are scoped based on historical cloud velocities. With its well-designed deep learning architecture, the proposed model can output multi-step-ahead prediction results sequentially by shifting receptive attention to dynamic ROIs. According to comparisons with related studies, the proposed model outperforms persistence and derived methods, and enhances its learning capability relative to conventional learning models via the novel architecture. The model can be applied to PV plants or arrays in different areas, suitable for forecast horizons within three hours.

中文翻译：

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直接从感兴趣区域的卫星图像学习短期太阳能预测


发展太阳能发电技术是缓解全球环境危机的有效途径。然而，太阳能是一种具有很强不确定性的能源，在实现准确的太阳能预测之前，这限制了光伏（PV）的大规模应用。基于现有的卫星太阳辐照度预测，光伏发电预测方法已得到广泛研究，而直接根据卫星图像对云运动进行建模仍然是一项艰巨的任务。在这项研究中，提出了一种端到端的短期预测模型，以卫星图像作为输入，它可以从堆叠的光流图中学习云运动特征。为了减少测量的巨大规模，静态感兴趣区域 (ROI) 根据历史云速度确定范围。凭借其精心设计的深度学习架构，该模型可以通过将接受注意力转移到动态 ROI 来顺序输出多步提前预测结果。根据与相关研究的比较，所提出的模型优于持久性和派生方法，并通过新颖的架构增强了相对于传统学习模型的学习能力。该模型可应用于不同区域的光伏电站或阵列，适合三小时内的预测范围。