IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i21p5674-d437374.html
   My bibliography  Save this article

Knowledge-Based Segmentation to Improve Accuracy and Explainability in Non-Technical Losses Detection

Author

Listed:
  • Albert Calvo

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

  • 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)

  • Marta Arias

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

Abstract

Utility companies have a great interest in identifying energy losses. Here, we focus on Non-Technical Losses (NTL), which refer to losses caused by utility theft or meter errors. Typically, utility companies resort to machine learning solutions to automate and optimise the identification of such losses. This paper extends an existing NTL-detection framework: by including knowledge-based NTL segmentation, we have detected some opportunities for improving the accuracy and the explanations provided to the utility company. Our improved models focus on specific types of NTL and therefore, the explanations provided are easier to interpret, allowing stakeholders to make more informed decisions. The improvements and results presented in the article may benefit other industrial frameworks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5674-:d:437374
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/21/5674/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/21/5674/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    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.

    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:13:y:2020:i:21:p:5674-:d:437374. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.