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dc.creatorMonedero Goicoechea, Iñigo Luises
dc.creatorBiscarri Triviño, Félixes
dc.creatorLeón de Mora, Carloses
dc.creatorBiscarri Triviño, Jesúses
dc.creatorMillán, Rocíoes
dc.date.accessioned2018-07-05T09:13:05Z
dc.date.available2018-07-05T09:13:05Z
dc.date.issued2006
dc.identifier.citationMonedero Goicoechea, I.L., Biscarri Triviño, F., León de Mora, C., Biscarri Triviño, J. y Millán, R. (2006). MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques. En ICCSA 2006: International Conference on Computational Science and Its Applications (725-734), Glasgow, UK: Springer.
dc.identifier.isbn978-3-540-34079-9es
dc.identifier.issn0302-9743es
dc.identifier.urihttps://hdl.handle.net/11441/76833
dc.description.abstractDatamining has become increasingly common in both the public and private sectors. A non-technical loss is defined as any consumed energy or service which is not billed because of measurement equipment failure or ill-intentioned and fraudulent manipulation of said equipment. The detection of non-technical losses (which includes fraud detection) is a field where datamining has been applied successfully in recent times. However, the research in electrical companies is still limited, making it quite a new research topic. This paper describes a prototype for the detection of non-technical losses by means of two datamining techniques: neural networks and statistical studies. The methodologies developed were applied to two customer sets in Seville (Spain): a little town in the south (pop: 47,000) and hostelry sector. The results obtained were promising since new non-technical losses (verified by means of in-situ inspections) were detected through both methodologies with a high success rate.es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofICCSA 2006: International Conference on Computational Science and Its Applications (2006), p 725-734
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleMIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniqueses
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Tecnología Electrónicaes
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/11751649_80es
dc.identifier.doi10.1007/11751649_80es
idus.format.extent10es
dc.publication.initialPage725es
dc.publication.endPage734es
dc.eventtitleICCSA 2006: International Conference on Computational Science and Its Applicationses
dc.eventinstitutionGlasgow, UKes
dc.relation.publicationplaceBerlínes
dc.identifier.sisius6535522es

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Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional