Reference : Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/28370
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
English
Glauner, Patrick mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Boechat, Andre [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Dolberg, Lautaro [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bettinger, Franck [CHOICE Technologies Holding Sàrl]
Rangoni, Yves [CHOICE Technologies Holding Sàrl]
Duarte, Diogo [CHOICE Technologies Holding Sàrl]
2016
Proceedings of the Seventh IEEE Conference on Innovative Smart Grid Technologies (ISGT 2016)
Yes
International
Seventh IEEE Conference on Innovative Smart Grid Technologies (ISGT 2016)
from 06-09-2016 to 09-09-2016
IEEE
Minneapolis
USA
[en] Electricity Theft Detection ; Fuzzy Logic ; Imbalanced Classification ; Non-Technical Losses ; Support Vector Machine
[en] Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.
http://hdl.handle.net/10993/28370

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