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    TLC Pointer – THE USE OF GEOSPATIAL DATA FOR NON TECHNICAL LOSSES DETECTION

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    CIRED 2019 - 1798.pdf (267.8Kb)
    Paper number
    1798
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
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    Authors
    Santi, Paolo, Senseable City Lab- Massachusetts Institute of Technology, USA
    Zerbi, Massimo, Enel Global Infrastructures And Networks, Italy
    Ratti, Carlo, Senseable City Lab Massachusetts Institute of Technology, USA
    Tresoldi, Domenico, Enel Global Infrastructures And Networks, Italy
    Papa, Carlo, Enel Foundation, Italy
    Montesano, Giuseppe, Enel Foundation, Italy
    Abstract
    Several machine-learning models specialized in providing revenue protection for power distribution companies can be found in the literature. However, traditional approaches present some limits: those models, relying solely on internal Company data, can be blind compared to some types of frequent anomalies, e.g. illegal connections, absence of consumption drops, etc. An innovative approach is the combination of proprietary data with third party data sets, preferably linked to geographical coordinates, thus representing a modeling of the territory. By integrating multiple sources of data, it is possible in principle to identify inconsistencies between activity patterns across data sets that would otherwise be impossible to identify by solely relying on proprietary data.In this paper, we instantiate this idea based on a combination of fine-grained smart meter consumption data with cellular phone data records. The rational for combining power consumption and cellular phone data is that both data sets have been proved to be good proxies of human activity. Hence, the identification of emergent activity patterns in the two data sets, and their spatio-temporal comparison, holds potential of substantially increasing the effectiveness of non-technical loss detection with respect to standard machine learning practices.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/580
    http://dx.doi.org/10.34890/805
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

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