Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier

Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier

Effective detection of electricity theft is essential to maintain power system reliability. With the development of smart grids, traditional electricity theft detection technologies have become ineffective to deal with the increasingly complex data on the users’ side. To improve the auditing efficie...

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Bibliographic Details
Main Authors:	Zhengwei Qu, Hongwen Li, Yunjing Wang, Jiaxi Zhang, Ahmed Abu-Siada, Yunxiao Yao
Format:	Article
Language:	English
Published:	MDPI AG 2020-04-01
Series:	Energies
Subjects:	smart grid nontechnical losses electricity theft detection synthetic minority oversampling technique K-means cluster random forest
Online Access:	https://www.mdpi.com/1996-1073/13/8/2039

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