IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v16y2019i1d10.1007_s10287-018-0325-x.html
   My bibliography  Save this article

Big data analytics: an aid to detection of non-technical losses in power utilities

Author

Listed:
  • Giovanni Micheli

    (University of Bergamo)

  • Emiliano Soda

    (CESI)

  • Maria Teresa Vespucci

    (University of Bergamo)

  • Marco Gobbi

    (CESI)

  • Alessandro Bertani

    (CESI)

Abstract

The great amount of data collected by the Advanced Metering Infrastructure can help electric utilities to detect energy theft, a phenomenon that globally costs over 25 billions of dollars per year. To address this challenge, this paper describes a new approach to non-technical loss analysis in power utilities using a variant of the P2P computing that allows identifying frauds in the absence of total reachability of smart meters. Specifically, the proposed approach compares data recorded by the smart meters and by the collector in the same neighborhood area and detects the fraudulent customers through the application of a Multiple Linear Regression model. Using real utility data, the regression model has been compared with other data mining techniques such as SVM, neural networks and logistic regression, in order to validate the proposed approach. The empirical results show that the Multiple Linear Regression model can efficiently identify the energy thieves even in areas with problems of meters reachability.

Suggested Citation

  • Giovanni Micheli & Emiliano Soda & Maria Teresa Vespucci & Marco Gobbi & Alessandro Bertani, 2019. "Big data analytics: an aid to detection of non-technical losses in power utilities," Computational Management Science, Springer, vol. 16(1), pages 329-343, February.
  • Handle: RePEc:spr:comgts:v:16:y:2019:i:1:d:10.1007_s10287-018-0325-x
    DOI: 10.1007/s10287-018-0325-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10287-018-0325-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10287-018-0325-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Russell Tatenda Munodawafa & Satirenjit Kaur Johl, 2019. "Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies," Sustainability, MDPI, vol. 11(15), pages 1-21, August.
    2. Pamir & Nadeem Javaid & Saher Javaid & Muhammad Asif & Muhammad Umar Javed & Adamu Sani Yahaya & Sheraz Aslam, 2022. "Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit," Energies, MDPI, vol. 15(8), pages 1-20, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:16:y:2019:i:1:d:10.1007_s10287-018-0325-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.