Bridging the gap between energy consumption and distribution through non-technical loss detection
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...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/177598 |
| Acceso en línea: | https://hdl.handle.net/2117/177598 https://dx.doi.org/10.3390/en12091748 |
| Access Level: | acceso abierto |
| Palabra clave: | Energy industries Energy consumption Machine learning Fraud detection Supervised systems Indústries energètiques Energia -- Consum Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Energies::Energia elèctrica |
| Sumario: | 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. |
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