Evaluating electricity theft detectors in smart grid networks

Abstract

Electricity theft is estimated to cost billions of dollars per year in many countries. To reduce electricity theft, electric utilities are leveraging data collected by the new Advanced Metering Infrastructure (AMI) and using data analytics to identify abnormal consumption trends and possible fraud. In this paper, we propose the first threat model for the use of data analytics in detecting electricity theft, and a new metric that leverages this threat model in order to evaluate and compare anomaly detectors. We use real data from an AMI system to validate our approach. © 2012 Springer-Verlag.