Anomaly Detection in Smart Grid Energy Data Using Machine Learning Techniques

Abstract

Smart grids generate huge amounts of energy information in real-time, which is vital to the grid's efficiency and reliability, but is often marred by anomalies due to faulty meters, equipment failures, and energy theft, resulting in substantial losses. This paper presents the development of an anomaly detection system named FlowTrack, which is production-ready and uses a hybrid machine learning model to identify and classify anomalies in energy information from smart grids. It uses Isolation Forest and LSTM Autoencoders to identify point and temporal anomalies, respectively, and has a web-based dashboard to visualize anomalies in real-time and a severity scoring system to prioritize critical anomalies. Experimentation results show that the FlowTrack system has high accuracy in detecting anomalies (93.6% F1-score), validating its deployment in today's smart grids