Electricity-theft detection in smart grids based on deep learning

Abstract

lectricity theft is a major concern for utilities. The smart grid (SG) infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning, and deep learning techniques can accurately identify electricity theft users. A convolutional neural network (CNN) model for automatic electricity theft detection is presented. This work considers experimentation to find the best configuration of the sequential model (SM) for classifying and identifying electricity theft. The best performance has been obtained in two layers with the first layer consists of 128 nodes and the second layer is 64 nodes. The accuracy reached up to 0.92. This enables the design of high-performance electricity signal classifiers that can be used in several applications. Designing electricity signals classifiers has been achieved using a CNN and the data extracted from the electricity consumption dataset using an SM. In addition, the blue monkey (BM) algorithm is used to reduce the features in the dataset. In this respect, the focusing of this work is to reduce the features in the dataset to obtain high-performance electricity signals classifier models.