ELECTRICITY THEFT DETECTION IN POWER GRIDS WITH DEEP LEARNING AND RANDOM FORESTS

Abstract

As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the
electricity theft causes significant harm to power grids, which influences power supply quality and
reduces operating profits. In order to help utility companies solve the problems of inefficient
electricity inspection and irregular power consumption, a novel hybrid convolutional neural
network-random forest (CNN-RF) model for automatic electricity theft detection is presented in
this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the
features between different hours of the day and different days from massive and varying smart
meter data by the operations of convolution and down sampling. In addition, a dropout layer is
added to retard the risk of over fitting, and the back propagation algorithm is applied to update
network parameters in the training phase. And then, the random forest (RF) is trained based on the
obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid
model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments
are conducted based on real energy consumption data, and the results show that the proposed
detection model outperforms other methods in terms of accuracy and efficiency.