Self-Supervised Cloud Classification with Patch Rotation Tasks (SSCC-PR)

Solar irradiance, which is closely influenced by cloud cover, significantly affects photovoltaic (PV) power generation efficiency. To improve cloud type recognition without relying on labeled data tasks, this paper proposes a self-supervised cloud classification method based on patch rotation prediction. In the Pre-training stage, unlabeled ground-based cloud images are augmented through blockwise rotation, and high-level semantic representations are learned via a Swin Transformer encoder. In the fine-tuning stage, these representations are adapted to the cloud classification task using labeled data. Experimental results show that our method achieves 96.61% accuracy on the RCCD and 90.18% on the SWIMCAT dataset, outperforming existing supervised and self-supervised baselines by a clear margin. These results demonstrate the effectiveness and robustness of the proposed approach, especially in data-scarce scenarios. This research provides valuable technical support for improving the prediction of solar irradiance and optimizing PV power generation efficiency.