摘要
在当前地基云图分类任务中,存在识别准确率低等问题。为了提高云分类的精度,有效融合深度可分离卷积、注意力机制和残差结构的特点,构建DAR-CapsNet地基云图分类模型。首先,收集整理美国国家新能源实验室公开数据库中的地基云图,构建云分类数据库;然后,对所提出的DAR-CapsNet分类模型进行训练优化;最后,在不同数据集上验证所提出的分类模型性能。实验结果表明所提出的DAR-CapsNet分类模型,分类准确率高达95.50%,优于现有公开分类方法,且在不同数据集上具有较好的泛化性能。
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.
作者
魏亮
朱婷婷
过奕任
倪超
滕广
李岩
Wei Liang;Zhu Tingting;Guo Yiren;Ni Chao;Teng Guang;Li Yan(College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2023年第11期189-195,共7页
Acta Energiae Solaris Sinica
基金
国家自然科学基金青年项目(62006120)。
关键词
光伏发电
气象云
图像分类
卷积神经网络
机器学习
photovoltaic power generation
clouds
image classification
convolutional neural networks
machine learning
