A Novel Combined Data-Driven Approach for Electricity Theft Detection

TL;DR

This paper introduces a combined data-driven method using MIC and CFSFDP techniques to improve electricity theft detection accuracy in smart meter data, addressing limitations of existing methods.

Contribution

It proposes a novel framework that integrates MIC and CFSFDP for more effective detection of diverse electricity theft behaviors without requiring labeled data.

Findings

High detection accuracy demonstrated on Irish smart meter data

Effective identification of thefts with normal data shapes

Framework outperforms existing single-technique methods

Abstract

The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the Maximum Information Coefficient (MIC), which can find the correlations between the non-technical loss (NTL) and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.