Smart City Gnosys

Smart city article details

Title	An Energy Theft Detection Framework With Privacy Protection For Smart Grid
ID_Doc	8003
Authors	Xie R.
Year	2023
Published	Proceedings of the International Joint Conference on Neural Networks, 2023-June
DOI	http://dx.doi.org/10.1109/IJCNN54540.2023.10191166
Abstract	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. © 2023 IEEE.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
17767	0.931	Alshehri A.; Badr M.M.; Baza M.; Alshahrani H.	Deep Anomaly Detection Framework Utilizing Federated Learning For Electricity Theft Zero-Day Cyberattacks	Sensors, 24, 10 (2024)
50975	0.898	Gunduz M.Z.; Das R.	Smart Grid Security: An Effective Hybrid Cnn-Based Approach For Detecting Energy Theft Using Consumption Patterns	Sensors, 24, 4 (2024)
23974	0.891	Sowmya C.S.; Vibin R.; Mannam P.; Mounika L.; Kabat S.R.; Patra J.P.	Enhancing Smart Grid Security: Detecting Electricity Theft Through Ensemble Deep Learning	Proceedings of the 8th International Conference on Communication and Electronics Systems, ICCES 2023 (2023)
8606	0.889	Quasim M.T.; Nisa K.; Khan M.Z.; Husain M.S.; Alam S.; Shuaib M.; Meraj M.; Abdullah M.	An Internet Of Things Enabled Machine Learning Model For Energy Theft Prevention System (Etps) In Smart Cities	Journal of Cloud Computing, 12, 1 (2023)
10561	0.882	Sulaiman A.; Nagu B.; Kaur G.; Karuppaiah P.; Alshahrani H.; Reshan M.S.A.; AlYami S.; Shaikh A.	Artificial Intelligence-Based Secured Power Grid Protocol For Smart City	Sensors, 23, 19 (2023)
26322	0.878	Moayyed H.; Moradzadeh A.; Mohammadi-Ivatloo B.; Ghorbani R.	Federated Deep Learning Technique For Power And Energy Systems Data Analysis	Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems (2022)
23955	0.877	Rao S.P.C.; Swarnam S.; Kumar N.; Sheikameer Batcha S.; Sivalanka V.; Chakravarthi A.V.D.	Enhancing Smart City Efficiency By Mitigating Electricity Theft In Smart Grids Using Lightweight Dnn And Smo	3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025 (2025)
43181	0.875	Zhang X.-Y.; Cordoba-Pachon J.-R.; Guo P.; Watkins C.; Kuenzel S.	Privacy-Preserving Federated Learning For Value-Added Service Model In Advanced Metering Infrastructure	IEEE Transactions on Computational Social Systems, 11, 1 (2024)
23957	0.874	Hashim M.; Khan L.; Javaid N.; Ullah Z.; Shaheen I.	Enhancing Smart City Functions Through The Mitigation Of Electricity Theft In Smart Grids: A Stacked Ensemble Method	International Transactions on Electrical Energy Systems, 2024 (2024)
37138	0.871	Ali A.; Khan L.; Javaid N.; Bouk S.H.; Aldegheishem A.; Alrajeh N.	Mitigating Anomalous Electricity Consumption In Smart Cities Using An Ai-Based Stacked-Generalization Technique	IET Renewable Power Generation, 19, 1 (2025)