Smart City Gnosys

Smart city article details

Title	Enhancing Smart City Functions Through The Mitigation Of Electricity Theft In Smart Grids: A Stacked Ensemble Method
ID_Doc	23957
Authors	Hashim M.; Khan L.; Javaid N.; Ullah Z.; Shaheen I.
Year	2024
Published	International Transactions on Electrical Energy Systems, 2024
DOI	http://dx.doi.org/10.1155/2024/5566402
Abstract	Smart grid is the primary stakeholder in smart cities integrated with modern technologies as the Internet of Things (IoT), smart healthcare systems, industrial IoT, renewable energy, energy communities, and the 6G network. Smart grids provide bidirectional power and information flow by integrating many IoT devices and software. These advanced IOTs and cyber layers introduced new types of vulnerabilities and could compromise the stability of smart grids. Some anomalous consumers leverage these vulnerabilities, launch theft attacks on the power system, and steal electricity to lower their electricity bills. The recent developments in numerous detection methods have been supported by cutting-edge machine learning (ML) approaches. Even so, these recent developments are practically not robust enough because of the limitations of single ML approaches employed. This research introduced a stacked ensemble method for electricity theft detection (ETD) in a smart grid. The framework detects anomalous consumers in two stages; in the first stage, four powerful classifiers are stacked and detect suspicious activity, and the output of these consumers is fed to a single classifier for the second-stage classification to get better results. Furthermore, we incorporate kernel principal component analysis (KPCA) and localized random affine shadow sampling (LoRAS) for feature engineering and data augmentation. We also perform comparative analysis using adaptive synthesis (ADASYN) and independent component analysis (ICA). The simulation findings reveal that the proposed model outperforms with 97% accuracy, 97% AUC score, and 98% precision. © 2024 Muhammad Hashim et al.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
22880	0.934	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)
37138	0.932	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)
23974	0.915	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)
25413	0.914	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)
23955	0.913	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)
2991	0.903	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)
58117	0.901	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)
14289	0.897	Naeem H.; Ullah F.; Srivastava G.	Classification Of Intrusion Cyber-Attacks In Smart Power Grids Using Deep Ensemble Learning With Metaheuristic-Based Optimization	Expert Systems, 42, 1 (2025)
8616	0.897	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)
8606	0.896	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)