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• The multi-task learning is used to simultaneously identify electricity theft users and detect their electricity theft time period. • The proposed model named MDRN is designed by using residual network, and the hyper-parameters of the model are selected by grid search. • A multi-task loss with task uncertainty is proposed to automatically balance the weights of two tasks during the training process, which improves the comprehensive performance of the multi-task model. • The effectiveness of the proposed method is verified using the Irish data set. To demonstrate the excellent performance of the method, we compare the proposed method with popular deep learning (DL) algorithms. Electricity theft is an economic problem faced around the power companies. After the suspicious electricity theft users are recognized, the potentially stolen electricity (PSE) and the loss caused by the electricity theft needs to be accurately evaluated. In the actual inspection work, the key to evaluating PSE is electricity theft time period detection. However, most of the current electricity theft detection algorithms can only recognize electricity theft, which cannot detect the specific time period of electricity theft simultaneously. Therefore, this paper proposes a deep model named multi-task deep residual network (MDRN), which can simultaneously recognize electricity theft users and detect their electricity theft time period. The MDRN is constructed based on one-dimensional convolutional and residual network, which can effectively extract characteristics from power consumption data. To automatically balance the multiple tasks in training, a joint multi-task loss with task uncertainty is proposed. The experimental results based on the Irish dataset show that the proposed multi-task model obtains the highest Accuracy with 93.17% in electricity theft recognition and the highest intersection-over-union (IOU) with 76.58% in time period detection. It should be noted that the proposed method can be directly used to calculate the PSE to maximize economic return of electricity theft inspection. [ABSTRACT FROM AUTHOR]

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. • Construct an improved Transformer network-based model to detect electricity theft. • Use conv-attentional network to embed electricity consumption data. • Eliminate the effect of class imbalance by selecting a suitable loss function. • Solve the problem of model overfitting by adding various network structures. • Conduct sufficient experiments to verify the performance of the proposed model. [ABSTRACT FROM AUTHOR]