Energy Theft Detection
Energy theft detection in smart grids aims to identify fraudulent manipulation of energy consumption data to avoid payment, safeguarding grid operators' finances and system performance. Recent research emphasizes unsupervised machine learning techniques, particularly exploring denoising diffusion probabilistic models and ensemble methods that combine different anomaly detection approaches (e.g., reconstruction and forecasting errors) to improve accuracy, especially for users exhibiting highly variable energy consumption patterns. These advancements are crucial for enhancing the security and reliability of smart grids, offering practical solutions to a significant economic and operational challenge.
Papers
November 11, 2024