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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
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    Citations

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    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.

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