Deep Learning Models for Efficient Detection of Electricity Fraud in Smart Grids

—Electricity theft is one of the biggest problems facing energy companies. These companies eventually claimed that the conventional methods for preventing electricity fraud were insufficient, which led to the creation of systems based on artificial intelligence to identify thieves among electricity consumers. In this paper, we propose a system based on deep learning to identify customers who have engaged in fraudulent activity on smart grids. We selected one-dimensional (1D) and twodimensional (2D) convolutional neural network models from deep learning models to achieve our objective. Also, we proposed a new method to fill in missing values in the data set. Our findings show that our models enhance the performance of systems that identify electricity thieves.