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MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques
dc.creator | Monedero Goicoechea, Iñigo Luis | es |
dc.creator | Biscarri Triviño, Félix | es |
dc.creator | León de Mora, Carlos | es |
dc.creator | Biscarri Triviño, Jesús | es |
dc.creator | Millán, Rocío | es |
dc.date.accessioned | 2018-07-05T09:13:05Z | |
dc.date.available | 2018-07-05T09:13:05Z | |
dc.date.issued | 2006 | |
dc.identifier.citation | Monedero 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.isbn | 978-3-540-34079-9 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/76833 | |
dc.description.abstract | Datamining 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.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | ICCSA 2006: International Conference on Computational Science and Its Applications (2006), p 725-734 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/11751649_80 | es |
dc.identifier.doi | 10.1007/11751649_80 | es |
idus.format.extent | 10 | es |
dc.publication.initialPage | 725 | es |
dc.publication.endPage | 734 | es |
dc.eventtitle | ICCSA 2006: International Conference on Computational Science and Its Applications | es |
dc.eventinstitution | Glasgow, UK | es |
dc.relation.publicationplace | Berlín | es |
dc.identifier.sisius | 6535522 | es |