Anomaly Detection in Smart Electric Meters for Detecting Faults and Misuse of Electric Energy Consumption

Abstract

The emergence of Smart Electric Meters (SEMs) has revolutionized energy management, providing real-time data collection and monitoring capabilities. However, ensuring the accuracy and security of this data presents challenges. Anomaly detection in electric energy consumption patterns is crucial for identifying issues like technical faults, erroneous billing, and misuse of electricity. Our project offers an Anomaly Detection System that leverages data analytics and machine learning to scrutinize SEM data. By analyzing historical patterns, the system distinguishes anomalies from routine fluctuations, triggering alerts when irregularities are detected. This system enhances the reliability and security of electric energy consumption data, fostering a more efficient and sustainable energy sector. Key Words: Anomaly Detection, Smart electric Meters, Fault Detection, Energy Misuse.