Electricity Theft Risk Assessment and Mitigation Using Smart Meter, IoT, AI, AMI and Automatic Power Disconnection
Keywords:
Electricity Theft Detection, Smart Meter, Internet of Things (IoT), Artificial Intelligence (AI), Advanced Metering Infrastructure (AMI), Automatic Power Disconnection, Energy Monitoring, Risk Assessment.Abstract
Electricity theft is a significant issue that causes financial losses to electricity distribution authorities and affects the efficiency of energy management systems. Conventional energy meters have limited capability to identify unauthorized power consumption, meter tampering and illegal connections in real time. This paper proposes an advanced Electricity Theft Risk Assessment and Mitigation System using Smart Meter, Internet of Things (IoT), Artificial Intelligence (AI), Advanced Metering Infrastructure (AMI) and Automatic Power Disconnection. The smart meter continuously measures electrical parameters such as voltage, current, power and energy consumption. These measurements are transmitted through IoT communication modules to a centralized AMI platform for monitoring and analysis. AI-based algorithms process real-time and historical consumption data to detect abnormal usage patterns and identify potential theft activities with improved accuracy. The system assesses theft risk by comparing consumer-side measurements with authorized energy consumption records. Upon confirmation of suspicious activity, an automatic power disconnection mechanism isolates the connection to prevent further energy loss. The proposed system enables real-time monitoring, intelligent theft detection, rapid response, enhanced security and effective revenue protection. The integration of Smart Meter, IoT, AI, AMI and Automatic Power Disconnection provides a reliable and scalable solution for electricity theft prevention and efficient energy management.
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