Suche nach „[D.] [Duarte]“ hat 2 Publikationen gefunden
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    DigitalF: Angewandte Informatik

    Beitrag (Sammelband oder Tagungsband)

    Patrick Glauner, N. Dahringer, O. Puhachov, J. Meira, P. Valtchev, R. State, D. Duarte

    Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations

    Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW 2017) [November 18-21, 2017; New Orleans, LA, USA]


    DOI: 10.1109/ICDMW.2017.40

    Abstract anzeigen

    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.

    DigitalF: Angewandte Informatik

    Beitrag (Sammelband oder Tagungsband)

    Patrick Glauner, A. Boechat, L. Dolberg, R. State, F. Bettinger, Y. Rangoni, D. Duarte

    Large-scale detection of non-technical losses in imbalanced data sets

    Proceedings of the 2016 Seventh IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2016) [September 6-9, 2016; Minneapolis, MN, USA]


    DOI: 10.1109/ISGT.2016.7781159

    Abstract anzeigen

    Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.