Energy Theft Detection in Smart Grids Using Graph Neural Networks (GNNs)

Abstract-Energy theft poses significant challenges to smart grid operations, leading to substantial financial losses and grid instability. Traditional machine learning approaches often fall short in detecting energy theft due to the complex and interconnected nature of smart grid systems. This paper proposes a novel approach to energy theft detection using Graph Neural Networks (GNNs), leveraging the inherent graph structure of smart grids. By representing the grid as a graph, where nodes correspond to smart meters and transformers, and edges represent electrical connections, GNNs capture both the local consumption patterns and the relationships between grid components. The proposed model aggregates node and edge features to identify anomalous consumption behaviors indicative of energy theft. We apply both Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to enhance detection accuracy by considering both the structural and consumption-related features of the grid. The model is trained and evaluated on real-world and simulated smart grid datasets, showing improved performance over traditional classification models such as support vector machines and random forests. Evaluation metrics including precision, recall, and F1-score demonstrate the model’s robustness, even in the presence of noisy data and imbalanced class distributions. This research highlights the potential of GNNs to enhance energy theft detection in smart grids, providing a scalable and interpretable solution that can adapt to evolving grid conditions. Future work includes expanding the model to incorporate temporal data for real-time detection and exploring reinforcement learning for adaptive theft prevention strategies.