Energy Theft Detection Using Graph Neural Networks on Distribution Network Topology

Abstract

Non-technical losses from energy theft account for 15-25% of electricity distributed in many Indian states, representing both a significant revenue gap for utilities and a barrier to grid modernization. Existing ML-based theft detection approaches treat each consumer independently, ignoring the network topology that connects them. We propose a graph neural network (GNN) approach that models the distribution network as a graph where consumers are nodes and electrical connectivity provides edges. The GNN aggregates consumption patterns from electrically adjacent consumers, enabling detection of theft patterns that are invisible when consumers are analyzed in isolation, such as coordinated meter tampering on the same lateral or theft downstream of a tampered distribution transformer. Evaluated on data from a large Indian distribution utility covering 1.2 million consumers, our approach achieves 89% precision and 76% recall for theft detection, outperforming independent consumer models by 23% in precision and 31% in recall. The paper discusses the practical considerations for deployment in Indian utility IT environments, including integration with existing billing systems and the handling of incomplete network topology data.

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