Securing Smart Grids: A Triplet Loss Function Siamese Network-Based Approach for Detecting Electricity Theft in Power Utilities

Electricity theft in power grids results in significant economic losses for utility companies. While machine learning (ML) methods have shown promising results in detecting such frauds, they often suffer from low detection rates, leading to excessive physical inspections. In this study, we attempted to solve the above-mentioned problem using a novel approach. The proposed framework utilizes the intelligence of Siamese network architecture with the Triplet Loss function to detect electricity theft using a labeled dataset obtained from Multan Electric Power Company (MEPCO), Pakistan. The proposed method involves analyzing and comparing the consumption patterns of honest and fraudulent consumers, enabling the model to distinguish between the two categories with enhanced accuracy and detection rates. We incorporate advanced feature extraction techniques and data mining methods to transform raw consumption data into informative features, such as time-based consumption profiles and anomalous load behaviors, which are crucial for detecting abnormal patterns in electricity consumption. The refined dataset is then used to train the Siamese network, where the Triplet Loss function optimizes the model by maximizing the distance between dissimilar (fraudulent and honest) consumption patterns while minimizing the distance among similar ones. The results demonstrate that our proposed solution outperforms traditional methods by significantly improving accuracy (95.4%) and precision (92%). Eventually, the integration of feature extraction with Siamese networks and Triplet Loss offers a scalable and robust framework for enhancing the security and operational efficiency of power grids.