Eletricity Theft Detection In Smart Grids Based On Deep Neural Network

Electricity theft is a significant challenge for power utility companies, particularly in regions where smart grid technology is being adopted.Smart grids offer an advanced infrastructure with real-time monitoring and analytics capabilities; however, they also present new vulnerabilities to non-technical and technical electricity theft.Traditional detection methods rely heavily on manual inspections or rule-based systems that are not scalable and often fail to adapt to evolving theft strategies.In this study, we propose a deep neural network (DNN)-based approach for detecting electricity theft within smart grid systems.By leveraging the high-dimensional data generated by smart meters, including consumption patterns over time, our model learns complex, non-linear relationships and anomalies that signal fraudulent activity.The dataset used includes labeled electricity usage data under both normal and theft conditions.Preprocessing techniques such as normalization and feature engineering are applied to enhance model performance.The proposed DNN model is trained and validated using multiple evaluation metrics including accuracy, precision, recall, and F1-score.Results demonstrate that the DNN-based system significantly outperforms traditional machine learning models such as Decision Trees and Support Vector Machines in terms of detection accuracy and robustness to new, unseen theft patterns.This research contributes to the field of smart grid security by providing a scalable, data-driven solution for electricity theft detection, paving the way for more intelligent and adaptive energy distribution networks.The model's success emphasizes the potential of deep learning in enhancing the reliability and economic efficiency of smart grid infrastructures.