Smart City Gnosys

Smart city article details

Title	Enhancing Smart City Efficiency By Mitigating Electricity Theft In Smart Grids Using Lightweight Dnn And Smo
ID_Doc	23955
Authors	Rao S.P.C.; Swarnam S.; Kumar N.; Sheikameer Batcha S.; Sivalanka V.; Chakravarthi A.V.D.
Year	2025
Published	3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025
DOI	http://dx.doi.org/10.1109/ICICACS65178.2025.10968803
Abstract	Smart cities require smart grid integration, industrial IoT, energy communities, renewable energy, smart healthcare systems, and the 6G network. Smart grids enable two-way power and data communication between various IoT devices and apps. However, increasing cyber layers and IoT connection threaten grid stability. Miscreants use these loopholes to illegally cut their power costs. The recommended approach includes model training, feature extraction, and preprocessing. Fixing outliers, standardizing units, and filling missing data are preprocessing steps. Feature extraction emphasizes majority class samples while considering minority class samples to account for the power consumption dataset's class imbalance. During model training, a lightweight deep neural network (DNN) addresses these difficulties. The Lightweight DNN model outperforms DNN and CNN for power theft detection. It improved smart grid power theft defenses to 94.28% accuracy. Smart grid electricity theft hinders smart city growth, but this study indicates that the Lightweight DNN model can handle it. The method ensures detection accuracy and grid stability, helping smart city infrastructures integrate current technology sustainably. © 2025 IEEE.
Author Keywords	deep neural network (DNN); electricity in power grids; smart city

Similar Articles

Id	Similarity	Authors	Title	Published
23957	0.913	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)
8616	0.911	Nandhini N.; Manikandan V.; Elango S.	An Interpretable Generalized Additive Neural Networks For Electricity Theft Detection In Smart Cities Using Balanced Data And Intelligent Grid Management	Energy and Buildings, 346 (2025)
50975	0.896	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)
37138	0.894	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)
8606	0.887	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)
2991	0.884	Aslam Z.; Javaid N.; Javed M.U.; Aslam M.; Aldegheishem A.; Alrajeh N.	A New Clustering-Based Semi-Supervised Method To Restrict The Users From Anomalous Electricity Consumption: Supporting Urbanization	Electrical Engineering, 106, 5 (2024)
23974	0.883	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)
58117	0.881	Arif A.; Alghamdi T.A.; Khan Z.A.; Javaid N.	Towards Efficient Energy Utilization Using Big Data Analytics In Smart Cities For Electricity Theft Detection	Big Data Research, 27 (2022)
25413	0.878	Ali A.; Khan L.; Javaid N.; Aslam M.; Aldegheishem A.; Alrajeh N.	Exploiting Machine Learning To Tackle Peculiar Consumption Of Electricity In Power Grids: A Step Towards Building Green Smart Cities	IET Generation, Transmission and Distribution, 18, 3 (2024)
22880	0.878	Pamir; Javaid N.; Akbar M.; Aldegheishem A.; Alrajeh N.; Mohammed E.A.	Employing A Machine Learning Boosting Classifiers Based Stacking Ensemble Model For Detecting Non Technical Losses In Smart Grids	IEEE Access, 10 (2022)