| Summary: | With the increasing installed capacity of renewable energy sources such as photovoltaic (PV) generation in the power system, ultra-short-term PV power prediction is more important for the stability. However, the prediction accuracy in the traditional methods cannot be guaranteed. Based on multi-exposure high-resolution total sky images (TSIs), this paper presents an advanced ultra-short-term PV prediction method using deep learning. To fully utilize the high-resolution images, an overlapping sliding window cutting and concatenating strategy are described to capture the global and local features of an image. The multi-exposure images are fused to provide more details about edge information and brightness. To better extract the image features and sequential features for PV prediction, a convolutional long–short-term memory model (CLSTM) with a multi-head self-attention mechanism is presented. The experiments use real-world datasets in the Zero Carbon Emission laboratory at Zhejiang University. The simulation results show that the proposed model can utilize TSIs to achieve the desired accuracy consistently. Under different weather conditions, the prediction accuracy of this model is improved by 49.1%–66% compared with that of other models.
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