Volume 2 - Issue 3, May - June 2026

The rapid growth of smart technologies and urbanization has led to a significant increase in energy consumption and the generation of large volumes of energy usage data. Detecting abnormal energy consumption patterns is essential for improving efficiency, reducing wastage, and preventing issues such as equipment faults or unauthorized usage. Traditional methods based on manual monitoring and fixed thresholds are inefficient and fail to identify complex patterns This project presents a Smart Energy Consumption Anomaly Detection System using machine learning techniques. The system collects energy usage data from smart meters, preprocesses it, and analyzes consumption patterns to detect anomalies. Algorithms such as Isolation Forest, Random Forest, and Support Vector Machine (SVM) are used to improve detection accuracy. The system provides real-time alerts and visualizations, helping users and administrators take timely actions. Overall, the system offers an intelligent, scalable, and cost-effective solution for energy management.

Madhan Kumar M, Dr.M. Usha Devi,"Smart Energy Consumption Anomaly Detection System" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(815-820) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.