A novel approach to detect electricity theft based on conv-attentional Transformer Neural Network

With the establishment of advanced metering infrastructure (AMI), more and more deep learning (DL) methods are being used for electricity theft detection. However, there are still problems such as the construction of a DL model that better fits the theft detection task, sample imbalance in the data set and overfitting of the model which limit the potential of DL models. Therefore, a novel end-to-end solution based on DL to solve these problems for electricity theft detection is proposed in this paper. First, we construct a model based on Transformer Neural Network (TNN), which extracts global features of consumption data by calculating the self-attention between each segment obtained by dividing the whole load sequence, and calculates the relative relationship between features for customer classification. Then, we refine the model to further improve its performance. Conv-attentional module is used to embed the input data and capture the local features in each segment. And we minimize the effect of sample imbalance by choosing a suitable loss function, and address the problem of model overfitting by adding normalization layers, dropout regularization and L2 regularization. In addition, grid search is used to determine the optimal values of the model hyper-parameters. Finally, the performance of the proposed model is verified by experiments using the Irish data set. The results show that our method is able to extract features more efficiently and thus has a higher true positive rate (TPR) with a lower false positive rate (FPR) than other state-of-the-art detectors, and it has strong robustness.