  Smart grids extremely rely on Information and Communications Technology (ICT)
and smart meters to control and manage numerous parameters of the network.
However, using these infrastructures make smart grids more vulnerable to cyber
threats especially electricity theft. Electricity Theft Detection (EDT)
algorithms are typically used for such purpose since this Non-Technical Loss
(NTL) may lead to significant challenges in the power system. In this paper, an
Ensemble Deep Convolutional Neural Network (EDCNN) algorithm for ETD in smart
grids has been proposed. As the first layer of the model, a random under
bagging technique is applied to deal with the imbalance data, and then Deep
Convolutional Neural Networks (DCNN) are utilized on each subset. Finally, a
voting system is embedded, in the last part. The evaluation results based on
the Area Under Curve (AUC), precision, recall, f1-score, and accuracy verify
the efficiency of the proposed method compared to the existing method in the
literature.
