CLOUD CLASSIFICATION OF GROUND-BASED CLOUD IMAGES BASED ON DAR-CapsNet

Wei Liang; Zhu Tingting; Guo Yiren; Ni Chao; Teng Guang; Li Yan

doi:10.19912/j.0254-0096.tynxb.2022-1099

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (11) : 189-195. DOI: 10.19912/j.0254-0096.tynxb.2022-1099

CLOUD CLASSIFICATION OF GROUND-BASED CLOUD IMAGES BASED ON DAR-CapsNet

Wei Liang, Zhu Tingting, Guo Yiren, Ni Chao, Teng Guang, Li Yan

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Abstract

In the current ground-based cloud image classification task, there are problems such as low recognition accuracy. In order to improve the accuracy of cloud classification, the DAR-CapsNet classification model for ground-based cloud images has been constructed by effectively integrating the features of depthwise separable convolution, attention mechanism and residual structure. Firstly, the ground-based cloud images were collected from the public database of the National New Energy Laboratory of the United States to build a cloud classification database; then, the proposed DAR-CapsNet classification model was trained and optimized; finally, experiments were conducted on different datasets to verify the performance of the proposed classification model. The experimental results show that the classification accuracy of the DAR-CapsNet model is as high as 95.50%, which is better than some published classification models, and the DAR-CapsNet model has better generalization performance on different datasets.

Key words

photovoltaic power generation / clouds / image classification / convolutional neural networks / machine learning

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Wei Liang, Zhu Tingting, Guo Yiren, Ni Chao, Teng Guang, Li Yan. CLOUD CLASSIFICATION OF GROUND-BASED CLOUD IMAGES BASED ON DAR-CapsNet[J]. Acta Energiae Solaris Sinica. 2023, 44(11): 189-195 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1099

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