IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1748-d229390.html
   My bibliography  Save this article

Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection

Author

Listed:
  • Bernat Coma-Puig

    (Department of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain)

  • Josep Carmona

    (Department of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain)

Abstract

The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides.

Suggested Citation

  • Bernat Coma-Puig & Josep Carmona, 2019. "Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection," Energies, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1748-:d:229390
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1748/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1748/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    2. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    3. Zhengwei Qu & Hongwen Li & Yunjing Wang & Jiaxi Zhang & Ahmed Abu-Siada & Yunxiao Yao, 2020. "Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier," Energies, MDPI, vol. 13(8), pages 1-20, April.
    4. Albert Calvo & Bernat Coma-Puig & Josep Carmona & Marta Arias, 2020. "Knowledge-Based Segmentation to Improve Accuracy and Explainability in Non-Technical Losses Detection," Energies, MDPI, vol. 13(21), pages 1-15, October.
    5. Marcelo Bruno Capeletti & Bruno Knevitz Hammerschmitt & Renato Grethe Negri & Fernando Guilherme Kaehler Guarda & Lucio Rene Prade & Nelson Knak Neto & Alzenira da Rosa Abaide, 2022. "Identification of Nontechnical Losses in Distribution Systems Adding Exogenous Data and Artificial Intelligence," Energies, MDPI, vol. 15(23), pages 1-23, November.
    6. Xiaoquan Lu & Yu Zhou & Zhongdong Wang & Yongxian Yi & Longji Feng & Fei Wang, 2019. "Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid," Energies, MDPI, vol. 12(18), pages 1-18, September.

    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:gam:jeners:v:12:y:2019:i:9:p:1748-:d:229390. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.