基于自学习边权重图卷积网络的用户用能分类

用户分类是用能分析的一种重要方法,而智能电表的广泛应用为用户用能分析提供了大量的可用数据.为进一步提高用户分类精度与用能特征的提取能力,本文提出了一种自学习边权重的图卷积网络.所提出的网络通过具有注意力机制的特殊初始化层将原始能耗数据转换为图,并从生成的图中提取能耗特征,最终根据图的学习特征输出用户类.为证明所提出方法的有效性,本文在实际用能数据集上进行了对比实验.实验结果表明,本文方法不仅能够更好地提取用户特征,而且取得了更好的分类性能.

User Energy Classification Based on Auto-learned Edge Weights Graph Convolution Network

User classification is an important method for energy consumption analysis, and the wide application of smart meters provides a large number of available data for user classification. To improve the accuracy of user classification and the extraction ability of energy consumption features, this study proposes a graph convolutional network (GCN) of self-learned edge weights for user classification. It converts the original energy consumption data into a graph through a special initialization layer with attention mechanisms and extracts energy consumption features from the generated graph. Then, the proposed network outputs the user classes according to the learning features of the graph. Through comparative experiments on a real energy consumption dataset, it is proven that the feature extraction of the proposed method is more intuitive and clear, and the classification performance of the proposed method is better than the existing methods.