Reference : Detection of Irregular Power Usage using Machine Learning

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/35607

Title :	Detection of Irregular Power Usage using Machine Learning
Language :	English
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) > >]
	State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	2018
Peer reviewed :	Yes
Event name :	IEEE Conference on Innovative Smart Grid Technologies, Asia (ISGT Asia 2018)
Event date :	from 22-05-2018 to 25-05-2018
Event country :	Singapore
Abstract :	[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 India, Pakistan, Malaysia, Brazil 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 machine learning and data mining have evolved as the primary direction to solve this problem. This course will present and compare different approaches reported in the literature. Practical case studies on real data sets will be included. As an additional outcome, attendees will understand the open challenges of NTL detection and learn how these challenges could be solved in the coming years.
Permalink :	http://hdl.handle.net/10993/35607

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