Electricity Theft Detection Using IoT

This paper presented the design, implementation, and laboratory evaluation of a low-cost IoT-based electricity theft detection system built around an ESP32 microcontroller, ZMPT101B voltage sensing, ACS712-20A current sensing, and MQTT-based cloud telemetry. The deviation-based detection algorithm, with a 15% threshold and three-window confirmation, identified theft taps exceeding approximately 15% of the reference power with a representative accuracy of 91.7%, precision of 85.7%, and recall of 90.0% over 60 monitoring runs, while correctly suppressing transient load disturbances and achieving an average alert latency of about 3.4 s. The missed detections corresponded to sub-threshold taps rather than sensing errors, and the false positives arose from borderline legitimate load changes, both consistent with the theoretical sensitivity floor of threshold-based detection. Key outcome. The system establishes a practical, replicable, and economically accessible foundation for automated NTL surveillance, particularly suited to distribution utilities in regions where electricity theft is most severe but large-scale smart-metering investment remains out of reach.

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