Reference : Introduction to Detection of Non-Technical Losses using Data Analytics
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/32255
Introduction to Detection of Non-Technical Losses using Data Analytics
English
Glauner, Patrick mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Meira, Jorge Augusto mailto [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) > >]
Mano, Rui [CHOICE Technologies Holding Sàrl]
Sep-2017
Yes
International
7th IEEE Conference on Innovative Smart Grid Technologies, Europe (ISGT Europe 2017)
from 26-09-2017 to 29-09-2017
IEEE
Torino
Italy
[en] Electricity losses are a frequently appearing problem in power grids. Non-technical losses (NTL) appear during distribution and include, but are not limited to, the following causes: Meter tampering in order to record lower consumptions, bypassing meters by rigging lines from the power source, arranged false meter readings by bribing meter readers, faulty or broken meters, un-metered supply, technical and human errors in meter readings, data processing and billing. NTLs are also reported to range up to 40% of the total electricity distributed in countries such as Brazil, India, Malaysia or Lebanon. This is an introductory level course to discuss how to predict if a customer causes a NTL. In the last years, employing data analytics methods such as data mining and machine learning have evolved as the primary direction to solve this problem. This course will compare and contrast different approaches reported in the literature. Practical case studies on real data sets will be included. Therefore, attendees will not only understand, but rather experience the challenges of NTL detection and learn how these challenges could be solved in the coming years.
Researchers ; Professionals
http://hdl.handle.net/10993/32255

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