Data‐Driven Detection of Integrated Energy Theft Based on VMD‐MIC and Informer

Abstract

AbstractIntegrated energy systems (IES) may experience energy theft through disinformation injection attacks. With electricity as the primary target, the attacks are essentially two way. They gain additional benefits by manipulating meter readings on both the consumer and supply side. Regression based deep learning models with feature engineering can be used to detect these attacks. To detect energy theft more efficiently, this paper proposes an integrated energy systems theft detection (IESTD) based on variational modal decomposition‐maximal information coefficient (VMD‐MIC) and Informer. And using kernel density estimation (KDE), the error between the predicted and true values is fitted as a probability density function (PDF).The thresholds for the confidence intervals of the PDF and the thresholds for the number of outliers are set as the detection metrics. Simulations are performed on the three generated datasets. The results show that the model has good detection accuracy and strong robustness.