Volume 10, Number 1

Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining
Knowledge Discovery Process


Blazakis Konstantinos and Stavrakakis Georgios, Technical University of Crete, Greece


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.


Data mining, Mean Shift clustering algorithm, Principal Component Analysis (PCA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Non-Technical Losses (NTLs), power theft, smart grid, smart electricity metering