Energy Theft Detection in Smart Grids Using Machine Learning: A Structured Review and Proposed Hybrid Framework

References

[1] A. O. Kolade, B. B. Adetokun, and O. Oghorada, "Energy Theft Detection in Power System Network: Reviews of Studies on Machine Learning Based Solutions," IEEE Conf. Paper, 2023.

[2] Lakshmi G and R. Gopal, "Design and Development of IoT Prototype for Real-Time Theft Detection and Optimization of Electricity Using Machine Learning Techniques," ICCDA, IEEE, 2024.

[3] S. N. Kumar, P. Rajesh, and K. S. Rao, "Machine Learning Based Electricity Theft Detection in Smart Grid Systems," IEEE Conf., 2022.

[4] G. R. Venkatakrishnan et al., "Anomaly Detection in Smart Metering: Clustering-Based Identification of Energy Theft," ICCCT, IEEE, 2025.

[5] Abilesh K. S. et al., "Revolutionizing Energy Management System with Machine Learning based Energy Demand Prediction and Theft Detection," ICACCS, IEEE, 2024.

[6] S. Abbas et al., "Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection," IEEE Access, vol. 12, 2024.

[7] S. Sahoo, R. Mishra, and P. K. Rout, "Machine Learning Based Detection of Electricity Theft in Smart Grid Using Consumption Pattern Analysis," ICPEC, IEEE, 2023.

[8] I. U. Khan, N. Javeid, C. J. Taylor, K. A. A. Gamage, and X. Ma, "A Stacked Machine and Deep Learning-Based Approach for Analysing Electricity Theft in Smart Grids," IEEE Trans. Smart Grid, vol. 13, no. 2, 2022.

[9] Deepa K R et al., "Accuracy Enhance of Smart Energy Theft Detection Using Machine Learning Classifiers," ICITEICS, IEEE, 2024.

[10] M. N. Amadhila, A. K. Shikongo, and H. Tjombonde, "Electricity Theft Detection Machine-Learning Models for Windhoek Informal Settlements," IEEE Conf., 2024.

[11] E. Altamimi et al., "Improving Energy Theft Detection through Time Series Segmentation and Ensemble Learning," IECON, IEEE, 2024.

[12] H. Lin, G. Zhang, and Z. Sun, "A Hybrid Machine Learning and Deep Learning Framework for Effective Electricity Theft Detection in Smart Grids," ACCTCS, IEEE, 2025.

[13] K. H. Akhil et al., "Enhanced Low Cost Smart Energy Meter with Theft Detection using IoT," ICIMIA, IEEE, 2023.

[14] R. S. Karthik, P. Deepa, M. Keerthana, and S. Harini, "IoT Based Smart Energy Meter for Electricity Theft Detection and Monitoring," IEEE Conf. Proc., 2024.

[15] A. M. Kaminski et al., "Assignment of AI Detected Buildings from Satellite Images to Registered Meters for Energy Theft Detection," ISGT, IEEE, 2024.

[16] M. M. Hasan, S. Ahmed, M. R. Islam, and T. Rahman, "Assignment of AI Detected Buildings from Satellite Images to Registered Meters for Energy Theft Detection," IEEE Conf. Proc., 2024.

[17] S. Ness, "Hybrid KNN-LSTM Framework for Electricity Theft Detection in Smart Grids Using SGCC Smart-Meter Data," IEEE Access, vol. 13, 2025.

[18] K. M. V. Gonzales et al., "Effect of Check Meter Quantity on Theft Detection in Distribution Networks with Rooftop PV and Net Metering," TENCON, IEEE, 2024.

[19] R. Qi, W. Japp, S. Pan, J. Zheng, and S. Shao, "Enhancing the Performance of Semi-supervised Electricity Theft Detection in Smart Grids with Feature Engineering and Ensemble Learning," KPEC, IEEE, 2024.

[20] T. Subburaj et al., "Enhancing Electricity Theft Detection and Loss Prediction in Metro Cities Using Hybrid Machine Learning Model," PDGC, IEEE, 2024.

[21] M. Ahammad and D. Md. Farid, "Detection of Electricity Theft Using Machine Learning Techniques," IEEE Conf. Proc., 2024.

[22] R. Thinakaran et al., "Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection," ICITDA, IEEE, 2024.

[23] R. M. Malkar et al., "A Hybrid Deep Learning and Machine Learning Approach for Optimizing Electricity Theft Detection," ICICNCT, IEEE, 2025.

[24] D. Lavanya, P. R. Karthikeyan, S. Nithya, and R. Prabha, "Electricity Theft Detection Using Machine Learning Techniques," ICSSET, IEEE, 2025.

[25] V. Karthik, S. Deepa, M. Harish, and R. Santhosh, "Intelligent Electricity Theft Detection System Using Machine Learning Algorithms," ICACCS, IEEE, 2025.

@article{1717927,
      author = {Shaik Heera, Dr. Haripriya V},
      title = {Energy Theft Detection in Smart Grids Using Machine Learning: A Structured Review and Proposed Hybrid Framework},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2551-2557},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717927.pdf},
      abstract = {Electricity theft is a critical challenge for global power utilities, causing annual losses estimated at USD 96 billion through meter tampering, illegal connections, and cyber manipulation of smart meter data. Traditional rule-based and manual inspection methods are inadequate against increasingly sophisticated theft techniques in modern smart grid environments. This paper presents a structured review of 25 IEEE-indexed publications (2022–2025) on machine learning-based energy theft detection, analysing methodologies, datasets, model architectures, and key limitations. Thematic classification identifies six major research directions: supervised classification, deep learning, hybrid and ensemble methods, IoT-integrated systems, anomaly detection, and satellite and geospatial AI approaches. Systematic gap analysis reveals ten critical deficiencies — including absence of real-time deployment, scalability constraints, limited temporal modelling, lack of automated alerts, and dataset imbalance — and maps fifteen targeted research questions and objectives. A novel intelligent hybrid ML-based energy theft detection framework is proposed, aimed at improving detection accuracy, enabling real-time automated alerting, and minimising electricity losses in smart grid environments.},
      keywords = {Energy Theft Detection, Non-Technical Losses, Smart Grid, Machine Learning, Deep Learning, LSTM, Anomaly Detection, SVM, Ensemble Learning, IoT, Smart Meter, SMOTE.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717927}
  }