# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 1 Introduction  
Affected by short-term weather changes, the output power of PV power generation is easily fluctuated [1, 2]. At present, the forecasting of PV power becomes an important method to reduce the impact of power fluctuations. Through power forecasting, the power sector can reasonably dispatch PV resources and reduce the impact of power fluctuations on grid-connected PVs. Dissipation and aggregation of cloud clusters in a short period of time are important factors that cause fluctuations in output power. Besides, solar irradiance is affected directly by the different types of clouds [3]. Different types of clouds have different characteristics, such as thickness, height, and sky coverage, which affect the magnitude of solar radiation received by the ground. Therefore, classification of clouds is crucial for PV power prediction. There are various forms of clouds belonging to the same category, and different categories of clouds are also a transitional relationship, so they have greater similarity, which brings great challenges to the classification of clouds. In the early days, machine learning based classifiers were often used to classify cloud images. For example, [PERSON] et al. [4] used the K-nearest neighbors to classify the cloud by extracting the spectral and texture features of the cloud image. [PERSON] et al. [5] introduced cloud classification by counting the color and texture features of cloud images, and at the same time considered multi-modal information as the input of the improved K-nearest neighbors classifier. [PERSON] et al. [6]proposed that the texture, local structure, and statistical feature were used as the input of SVM and achieved an accuracy of 64.1% on the dataset of nine cloud categories. With the continuous development of deep learning in recent years, the performance of convolutional neural network (CNN) in the field of image classification has been greatly improved. Because of better feature representation capabilities, CNN can mine more deep semantic features from the image. [PERSON] et al. [7] produced a MGCD dataset with 8000 ground-based cloud images and corresponding meteorological data, yielding an accuracy as high as 87.9% with multi-modal fusion algorithm. [PERSON] et al. [8] introduced a CNN to extract the features of cloud images; fisher vector coding and SVM classifier are utilized for cloud images classification. [PERSON] et al. [9] proposed a CloudNet model and obtained a high accuracy on a self-built CCSN dataset containing 2543 cloud images. [PERSON] et al. [10] proposed an ensemble learning algorithm to fuse the output probability vector of CNN and random forest classifier to improve the classification accuracy. In [11], the network named MMFN was proposed, which could learn extended cloud information by fusing heterogeneous features in a unified framework. In [12], the task-based graph convolutional network was introduced to obtain the correlation between cloud images, yielding an accuracy as high as 89.48%. There are many cloud image datasets available for reference in existing research. It can be divided into satellite cloud images, part-sky ground-based cloud images, and all-sky ground-based cloud images. There are many research methods for the classification of satellite cloud images [13; 14; 15; 16], and this classification is extremely effective for macro-level meteorological analysis. However, satellite cloud images cover a large area and have few local details. It is extremely difficult to analyze cloud clusters in a small patch of sky, which makes it not widely used in the field of PV power generation. For the classification of part-sky ground-based cloud images, there have also been many research methods [17; 18; 19]. However, part-sky ground-based cloud images have a small field of view and cannot meet the large-scale PV power station requirements.