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

Department of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Author to whom correspondence should be addressed.
Energies 2019, 12(9), 1748;
Received: 29 March 2019 / Revised: 25 April 2019 / Accepted: 30 April 2019 / Published: 8 May 2019
PDF [427 KB, uploaded 8 May 2019]


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. View Full-Text
Keywords: fraud detection; machine learning; supervised systems fraud detection; machine learning; supervised systems

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Coma-Puig, B.; Carmona, J. Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection. Energies 2019, 12, 1748.

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