AI-Assisted IoT System for Real-Time Electricity Theft

Electricity theft is a recurring global problem that prevents effective energy distribution and causes significant financial losses for utility corporations. Conventional detection techniques are inefficient, labor-intensive, and prone to errors since they frequently depend on human procedures. This study presents a novel Internet of Things (IoT)-based system for monitoring and detecting electricity theft in real time. Leveraging smart meters and IoT-enabled sensors, the system continuously monitors electricity usage to identify anomalies indicative of unauthorized consumption. Advanced machine learning algorithms analyze collected data to detect theft patterns with high precision. Upon identifying irregularities, the system promptly notifies utility providers and authorities, enabling swift corrective actions. The proposed solution offers a cost- effective and scalable approach for monitoring and securing power distribution networks, significantly reducing revenue losses and improving operational efficiency. Comprehensive simulations and real-world testing validate the system’s reliability, demonstrating its potential to revolutionize electricity theft prevention and enhance the sustainability of energy management systems.