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Distilling Provider-Independent Data for General Detection of Non-Technical Losses
MEIRA, Jorge Augusto; GLAUNER, Patrick; STATE, Radu et al.
2017In Power and Energy Conference, Illinois 23-24 February 2017
Peer reviewed
 

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Keywords :
Artificial intelligence; big data; electricity theft; feature engineering; mechine learning; non-technical losses
Abstract :
[en] Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These facts have directed the focus of our work to the NTL detection. Our approach is composed of two steps: 1) We compute several features and combine them in sets characterized by four criteria: temporal, locality, similarity and infrastructure. 2) We then use the sets of features to train three machine learning classifiers: random forest, logistic regression and support vector vachine. Our hypothesis is that features derived only from provider-independent data are adequate for an accurate detection of non-technical losses.
Disciplines :
Computer science
Author, co-author :
MEIRA, Jorge Augusto ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
GLAUNER, Patrick ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
STATE, Radu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
VALTCHEV, Petko ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
DOLBERG, Lautaro ;  CHOICE Technologies Holding Sàrl
Bettinger, Franck;  CHOICE Technologies Holding Sàrl
Duarte, Diogo;  CHOICE Technologies Holding Sàrl
External co-authors :
no
Language :
English
Title :
Distilling Provider-Independent Data for General Detection of Non-Technical Losses
Publication date :
2017
Event name :
Power and Energy Conference at Illinois 2017
Event date :
from 23-02-2017 to 24-02-2017
Audience :
International
Main work title :
Power and Energy Conference, Illinois 23-24 February 2017
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Available on ORBilu :
since 24 January 2017

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