Spatio-Temporal Graph Neural Networks for Real-Time Energy Theft Detection in Smart Grids

Abstract

Energy theft is a critical challenge faced by modern smart grids, leading to significant financial losses and operational inefficiencies. Traditional methods for detecting energy theft often fail to capture the intricate spatio-temporal dynamics of smart grid systems, such as the spatial dependencies among grid components and temporal fluctuations in consumption patterns. This paper introduces a novel Spatio-Temporal Graph Neural Network (ST-GNN) framework for real-time energy theft detection in smart grids. By modeling the grid as a dynamic graph, the proposed approach captures spatial relationships between grid entities (e.g., smart meters, transformers) and temporal variations in energy consumption data. The model employs graph convolutional layers for spatial feature extraction and recurrent or attention-based mechanisms for temporal trend analysis, enabling accurate and efficient anomaly detection. Experimental validation on real-world and synthetic datasets demonstrates the framework's ability to detect energy theft with high precision and low latency, offering a scalable and interpretable solution for enhancing smart grid security. This work provides a pathway for deploying intelligent, data-driven energy management systems that minimize non-technical losses and improve grid resilience.