Advanced Fraud Detection in Smart Grids Using Deep Neural Networks

Fraud detection in smart grids is critical to ensure reliable and efficient energy distribution, preventing significant financial losses and maintaining grid stability. This project presents an advanced fraud detection system utilizing deep neural networks (DNN) to automatically identify and prevent fraudulent activities such as energy theft, meter tampering, and unauthorized energy consumption. The DNN model processes vast amounts of data generated by smart meters and other grid sensors, learning complex patterns and anomalies that indicate potential fraud. By integrating feature extraction techniques and real-time monitoring, the system is designed for scalability and accuracy, capable of handling high-dimensional data from modern energy grids. Additionally, this solution improves the grid's resilience and operational efficiency by enabling proactive fraud detection and reducing false positives, helping utility companies safeguard their assets.