# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 4 Result and Discussion  
### Dataset for Classification  
The NRELCD and the MGCD dataset are used to prove the effectiveness of the method in this paper. The NRELCD dataset comes from the historical ground-based cloud images published by the National Renewable Energy Laboratory (NREL) in the United States. The image size is \(1024\times 1024\) pixels and the collection period is from 2018 to 2020. We screened 15,450 images with distinct category characteristics and analyzed the effect of each category on solar radiation attenuation to produce a dataset specific to the PV power sector. The amount of solar radiation received by the PV panels is a direct factor affecting the power generated by PV. According to the method described in the literature [28], we collected the all-day radiation for days when there was only one cloud genus in the sky and also for the nearest cloudless day to that day. As shown in Figure 7, the two data are compared to see the effect of each cloud category on the solar radiation attenuation. Nearly similar cloud types are combined according to the degree of attenuation of solar radiation by each type of clouds. Cloud categorization is not as clear as other categorization tasks. Clouds are sometimes in a transitional state and their classification is highly controversial. We did not use those transition state cloud images as part of the dataset but instead forced them to be classified in a practical application using a trained classifier. Since the cloud images in the NRELCD have continuity in time, the adjacent images are relatively similar. Random distribution of the dataset to the training set and the test set will cause the accuracy to be high, which is not in line with the actual situation. Therefore, the first 70% of the dataset is used as the training set and the rest as the test set according to the acquisition time. Different categories of clouds have different probabilities of occurring in different regions. In order to adapt the power prediction to the real environment, there are different cloud classification criteria. We try to test the classification ability of the model under different cloud classification criterias with two datasets with different categories. The MGCD dataset [7] also includes 7 categories, with a total of 8000 ground-based cloud images, each of which corresponds to 4 modals of weather data. The data format of cloud images is JPEG, and the size is 1024 \(\times\) 1024 pixels. The training set and the test set contain 4000 cloud image samples, respectively. We only use cloud image samples in the dataset for experiments. Mixed clouds in the MGCD dataset are composed of multiple categories of clouds. Objectively speaking, these combinations have different effects on solar radiation, so putting mixed clouds into one category is not reasonable in practice, but this classification criterion is informative for testing model performance. Table 1 shows the number of samples in each dataset, while Figures 8 and 9, respectively, listed the cloud samples of the two datasets. Figure 7: Effect of different cloud genera on solar radiation attenuation. Altocumulus and Cirrocumulus are infrequent, we have boxed the time of occurrence in red.