Abstract
Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. In this paper, a classification method of ground-based cloud images by improved combined convolutional network is proposed. To solve the problem of sub-network overfitting caused by redundancy of pixel information, overlap pooling kernel is used to enhance the elimination effect of information redundancy in the pooling layer. A new channel attention module, ECA-WS (Efficient Channel Attention–Weight Sharing), is introduced to improve the network’s ability to express channel information. The decision fusion algorithm is employed to fuse the outputs of sub-networks with multi-scales. According to the number of cloud images in each category, different weights are applied to the fusion results, which solves the problem of network scale limitation and dataset imbalance. Experiments are carried out on the open MGCD dataset and the self-built NRELCD dataset. The results show that the proposed model has significantly improved the classification accuracy compared with the classical network and the latest algorithms.
Highlights
Affected by short-term weather changes, the output power of PV power generation is fluctuated [1,2]
In the ICN model, accuracy of the MGCD dataset is increased by 2.03% and 2.88%, respectively, compared with the sub-networks ResNet50 and VGG16, while the accuracy of the NRELCD dataset is increased by 1.56% and 1.45%, respectively
The overfitting phenomenon is suppressed effectively on improved VGG16(VGG16 + OP + ECA-WS), and the parameter optimization of the two sub-networks is close to synchronization, which provides a good prerequisite for decision fusion
Summary
Affected by short-term weather changes, the output power of PV power generation is fluctuated [1,2]. Kazantzidis 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. In response to the above problems, we made a ground-based cloud images dataset with a larger amount of data and proposed a deep learning-based ground-based cloud image classification method. (2) A novel ground-based cloud images classification method by improved combined neural network is proposed; overlap pooling kernels are used in the sub-network to improve the effect of eliminating information redundancy and reduce the risk of overfitting. The rest of this paper is organized as follows: Section 2 briefly introduces some related work; Section 3 describes a novel ground-based cloud images classification method based on improved combined neural network; and Section 4 presents experimental results and some discussion. VGG16 replaces a larger size convolution kernel by stacking multiple 3 × 3 size convolution kernels, which ensures that the network can learn more complex nonlinear mapping modes while obtaining the same receptive field
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