Power Theft Detection Using Deep Neural Networks

-Theft detection in the power sector is a significant challenge for power distribution companies worldwide. The power losses are mainly due to dissipation from wires or theft done by manipulating energy meters or tapping cables at the consumer end. With power theft becoming a global issue, automatic detection of robbery is the need of the hour. This paper presents a deep learning-based solution for automated detection of power theft using consumers' consumption data. In this work, fully connected neural networks are trained on daily consumption data, and customized convolutional neural networks (CNN) and residual networks are trained on weekly consumption data. The models are evaluated using the area under the receiver operating characteristics curve (AUC) metric, which measures the degree of separation between the predicted classes. The results obtained on the real dataset indicate that residual networks provide better results than other methods, and ResNet34 outperforms the existing methods in the literature. The proposed system has a high potential to detect power theft in households, which can help the authorities cut down non-technical losses occurring in the power sector.