Electricity theft recognition and time period detection considering based on multi-task learning

Abstract

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.