  Energy theft constitutes an issue of great importance for electricity
operators. The attempt to detect and reduce non-technical losses is a
challenging task due to insufficient inspection methods. With the evolution of
advanced metering infrastructure (AMI) in smart grids, a more complicated
status quo in energy theft has emerged and many new technologies are being
adopted to solve the problem. In order to identify illegal residential
consumers, a computational method of analyzing and identifying electricity
consumption patterns of consumers based on data mining techniques has been
presented. Combining principal component analysis (PCA) with mean shift
algorithm for different power theft scenarios, we can now cope with the power
theft detection problem sufficiently. The overall research has shown
encouraging results in residential consumers power theft detection that will
help utilities to improve the reliability, security and operation of power
network.
