Electricity theft Detection with Borderline-SMOTE and WDCBL in the Smart Grid

Abstract

The non-technical loss caused by electricity theft on the user side not only increases the operationcost of the smart grid but also causes some harm to the electricity system. At present, the existingelectricity theft detection model ignores the time-series correlation of user power consumption data,and the distribution of positive and negative samples in the relevant data sets is uneven. Aiming atthe existing problems, we propose an electricity theft detection model based on Borderline-SMOTEand WDCBL(wide and deep CBL) networks. The WDCBL model consists of wide componentsand deep CBL components. The wide part uses a fully connected layer to extract one-dimensionalfeatures in user electricity consumption data. The model introduces the hybrid network model ofa one-dimensional convolutional neural network(CNN) and bidirectional long-term and short-termmemory network(BiLSTM) into the deep part, which can learn both spatial and temporal features inuser electricity consumption data. Finally, we conducted a comparative experiment on the user electricityconsumption data published by SGCC. The results show that our model is superior to othersin the comprehensive performance.