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

• Author(s): Shaik Heera; Dr. Haripriya V
• Paper ID: 1717927
• Page: 2551-2557
• Published Date: 19-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

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.

Citations

IRE Journals:
Shaik Heera, Dr. Haripriya V "Energy Theft Detection in Smart Grids Using Machine Learning: A Structured Review and Proposed Hybrid Framework" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2551-2557 https://doi.org/10.64388/IREV9I11-1717927

IEEE:
Shaik Heera, Dr. Haripriya V "Energy Theft Detection in Smart Grids Using Machine Learning: A Structured Review and Proposed Hybrid Framework" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717927