Anomaly Detection in Smart Metering: Clustering-Based Identification of Energy Theft

Energy theft and defective meter issues are significant challenges in power distribution systems, causing financial losses and operational inefficiencies. While traditional methods for detecting such anomalies are often limited in scope and accuracy, advancements in machine learning have opened up new possibilities. This project focuses on an automated, data-driven solution for detecting energy theft and defective meters, aiming to enhance revenue protection and system reliability. By leveraging algorithms such as linear regression, clustering, and predictive maintenance modeling, the system accurately identifies irregular consumption patterns indicative of theft or meter defects. The solution uses real-time consumption data to monitor deviations from expected usage, allowing for early detection and action. Key components include a comprehensive machine learning model utilizing RandomForest and ExtraTrees classifiers for theft detection, supported by K-Means clustering for pattern recognition and SMOTE for balancing data classes. The system also enables predictive maintenance scheduling by analyzing consumption trends to anticipate meter failures, optimizing operational efficiency. Data is securely stored and managed, facilitating real-time access for providers to monitor and respond to system irregularities effectively. The outcomes include accurate theft detection, minimized billing errors, improved predictive maintenance capabilities, and a scalable, user-friendly application for energy providers. This innovative approach not only aids in revenue optimization but also promotes a transparent and reliable energy distribution network.