在现有的云图分割研究中,大多数方法仅简单将天空和云层进行分割,忽视了不同类型云层之间的差异。且现有模型计算开销较大,运行效率较低,难以满足实时分割云图的需求,无法有效支持超短期光伏功率预测。针对此问题,提出一种轻量级云图细粒度分割模型CloudSwinNetLite。该模型采用编码-解码结构,以Swin Transformer作为基础模块,使用轻量级多头自注意力机制（W-GLWMSA）和基于Ghost的反向残差前馈网络（GIRFFN）,在保持网络分割性能的同时,实现计算轻量化。实验结果表明,CloudSwinNetLite与其他分割模型相比,能够在较低计算开销下实现对不同类型云层更加有效的分割。
Abstract
In existing cloud image segmentation research, most methods only distinguish between the sky and clouds, neglecting the inherent differences among various cloud types. In addition, many current models are computationally intensive and inefficient, making them unsuitable for the real-time segmentation of cloud images and thus limiting their application in ultra-short-term photovoltaic power forecasting. To overcome these challenges, we propose CloudSwinNetLite, a lightweight fine-grained cloud image segmentation network. The model adopts an encoder-decoder structure with Swin Transformer as the base module, using a lightweight multi-head self-attention mechanism （W-GLWMSA） and a ghost-based inverted residual feed-forward network （GIRFFN）. These designs jointly enhance computational efficiency while preserving high segmentation accuracy. Experimental results demonstrate that CloudSwinNetLite achieves effective fine-grained cloud segmentation with substantially reduced computational cost compared with existing models.
关键词
地基云图 /
光伏功率 /
预测 /
细粒度分割 /
轻量化网络
Key words
ground-based cloud image /
photovoltaic power /
forecasting /
fine-grained segmentation /
lightweight net
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基金
国家自然科学基金（62206095）; 河北省自然科学基金（F2024502017）; 中央高校基本科研业务费（2024MS117; 2023JG002）
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