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Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Glauner, Patrick; Meira, Jorge Augusto; Dolberg, Lautaro et al.
2016In Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT 2016)
Peer reviewed
 

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Keywords :
Data mining; Electricity theft detection; Feature engineering; Feature selection; Machine learning; Non-technical losses; Time series classification
Abstract :
[en] Electricity theft occurs around the world in both developed and developing countries and may range up to 40% of the total electricity distributed. More generally, electricity theft belongs to non-technical losses (NTL), which occur during the distribution of electricity in power grids. In this paper, we build features from the neighborhood of customers. We first split the area in which the customers are located into grids of different sizes. For each grid cell we then compute the proportion of inspected customers and the proportion of NTL found among the inspected customers. We then analyze the distributions of features generated and show why they are useful to predict NTL. In addition, we compute features from the consumption time series of customers. We also use master data features of customers, such as their customer class and voltage of their connection. We compute these features for a Big Data base of 31M meter readings, 700K customers and 400K inspection results. We then use these features to train four machine learning algorithms that are particularly suitable for Big Data sets because of their parallelizable structure: logistic regression, k-nearest neighbors, linear support vector machine and random forest. Using the neighborhood features instead of only analyzing the time series has resulted in appreciable results for Big Data sets for varying NTL proportions of 1%-90%. This work can therefore be deployed to a wide range of different regions.
Disciplines :
Computer science
Author, co-author :
Glauner, Patrick ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Meira, Jorge Augusto ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Dolberg, Lautaro;  CHOICE Technologies Holding Sàrl
State, Radu  ;  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
External co-authors :
yes
Language :
English
Title :
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Publication date :
2016
Event name :
3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT 2016)
Event place :
Shanghai, China
Event date :
from 06-12-2016 to 09-12-2016
Audience :
International
Main work title :
Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT 2016)
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Available on ORBilu :
since 07 October 2016

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