An Intelligent AI-Driven Framework For Electricity Theft Detection In Smart Grid Systems

Electricity theft is a major challenge faced by power distribution companies worldwide, leading to significant financial losses, reduced grid reliability, and increased safety risks. With the rapid deployment of smart grids and advanced metering infrastructure, large volumes of electricity consumption data are generated at regular intervals. Traditional theft detection methods based on manual inspection and rule-based analysis are inefficient, time-consuming, and incapable of identifying complex and concealed theft patterns. To overcome these limitations, this project proposes an Artificial Intelligence (AI)–based approach for detecting electricity theft by analyzing consumer energy usage behavior using smart meter data. The proposed system employs an Artificial Neural Network (ANN) model trained on historical electricity consumption data to identify abnormal patterns indicative of theft. Key statistical features such as mean, median, maximum, minimum, standard deviation, sum of energy consumption, and record count are extracted after thorough data preprocessing. The trained ANN model demonstrates high accuracy in distinguishing between normal and suspicious consumption behavior. A Flask-based web application is developed to provide a user-friendly interface for real-time prediction and visualization of results. This intelligent and scalable solution enhances grid security, minimizes non-technical losses, and supports efficient decision-making for utility providers, while also offering potential applicability in other anomaly detection domains.