Critical infrastructure; Non-technical losses; Time series classification; Microsoft HoloLens; Spatial hologram
Abstract :
[en] Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software.
Disciplines :
Computer science
Author, co-author :
Glauner, Patrick ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Dahringer, Niklas; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Puhachov, Oleksandr; 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)
Valtchev, Petko; University of Quebec in Montreal
State, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Duarte, Diogo; CHOICE Technologies Holding Sàrl
External co-authors :
yes
Language :
English
Title :
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Publication date :
2017
Event name :
17th IEEE International Conference on Data Mining Workshops (ICDMW 2017)
Event date :
18-11-2017 to 21-11-2017
Main work title :
Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017)
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