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

TL;DR

This paper presents a novel data mining approach combining PCA and mean shift algorithms to effectively detect power theft among residential consumers, addressing challenges posed by advanced metering infrastructure.

Contribution

It introduces a new computational method that integrates PCA with mean shift for improved detection of residential power theft scenarios.

Findings

Encouraging results in residential power theft detection

Enhanced reliability and security of power networks

Effective analysis of electricity consumption patterns

Abstract

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.