Ponencia
Increasing the efficiency in non-technical losses detection in utility companies
Autor/es | Guerrero Alonso, Juan Ignacio
León de Mora, Carlos Biscarri Triviño, Félix Monedero Goicoechea, Iñigo Luis Biscarri Triviño, Jesús Millán Navarro, María del Rocío |
Departamento | Universidad de Sevilla. Departamento de Tecnología Electrónica |
Fecha de publicación | 2010 |
Fecha de depósito | 2015-03-16 |
Publicado en |
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Resumen | Usually, the fraud detection method in utility
companies uses the consumption information, the economic
activity, the geographic location, the active/reactive ration and
the contracted power. This paper proposes a ... Usually, the fraud detection method in utility companies uses the consumption information, the economic activity, the geographic location, the active/reactive ration and the contracted power. This paper proposes a combined text mining and neural networks to increase the efficiency in NonTechnical Losses (NTLs) detection methods which was previously applied. This proposed framework proposes to collect all the information that normally cannot be treated with traditional methods. This framework is part of a research project. This project is done in collaboration with Endesa, one of the most important power distribution companies of Europe. Currently, the proposed framework is in the test stage and it uses real cases. |
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