An Energy Theft Detection Framework with Privacy Protection for Smart Grid

The integration of power grid with digital technologies has brought significant advancements to energy management, however, it has also heightened the risk of energy theft. To tackle this challenge, this study proposes a federated energy theft detection framework for smart grids.The proposed framework employs a federated learning system composed of a federated server and several distributed power companies. In this system, each power company can contribute to the improvement of energy theft detection without compromising data privacy. The deep learning model used in this framework is designed using Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU)-Attention.To evaluate the effectiveness of the proposed framework, a federated network communication gain metric has been proposed. The results of the experiments demonstrate that the proposed framework outperforms centralized approaches in terms of detection performance, particularly in the case of a single power company, where the accuracy of detection can increase by up to 2%. Furthermore, numerical results suggest that the proposed scheme exhibits considerable network communication gain and is suitable for the future smart cities.In conclusion, this study presents a novel federated energy theft detection framework that has the potential to improve the energy management system while preserving data privacy.