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@article{195671,
        author = {Danish Abubakar Khan and Mohammad Aliraza Muphid Koke and Munzir Bashir Shaikh and Shadan Talat Shaikh},
        title = {Electroguard: Energy Theft Detector},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1576-1587},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195671},
        abstract = {Electricity theft remains a major challenge for power distribution systems, leading to significant financial losses, grid instability, and inefficiencies in energy management. Traditional detection methods, including manual inspections and rule-based systems, are often inadequate due to their inability to adapt to evolving consumption patterns and hidden anomalies. This necessitates the development of intelligent, data-driven solutions capable of identifying irregular usage in real-time.
This research presents the development of an AI-driven system for electricity theft detection and prevention that leverages a multimodal and ensemble learning approach. The proposed system integrates diverse parameters such as regional characteristics, household structure, occupancy levels, appliance usage, environmental conditions, and consumption deviations to build a comprehensive understanding of energy usage behavior. Multiple machine learning models, including neural networks, random forest, and gradient boosting techniques, are combined within an ensemble framework to improve detection accuracy and robustness.
The system is implemented through a web-based interface that enables real-time analysis and user interaction, providing actionable insights into consumption patterns. By incorporating contextual and behavioral factors alongside numerical data, the model effectively distinguishes between legitimate variations in energy usage and suspicious activities. Experimental evaluation demonstrates that the system achieves high accuracy in detecting anomalous consumption patterns while minimizing false positives.
The findings suggest that integrating artificial intelligence with contextual data analysis can significantly enhance the detection of electricity theft compared to traditional approaches. This study contributes to the field of intelligent energy systems by proposing a scalable and practical solution that supports real-time monitoring, improves operational efficiency, and promotes fair energy distribution.},
        keywords = {Electricity Theft Detection; Machine Learning; Ensemble Learning; Smart Grid; Energy Analytics; Anomaly Detection; Real-Time Monitoring; AI in Energy Systems; Consumption Analysis; ElectroGuard System},
        month = {April},
        }