dc.creator | León de Mora, Carlos | es |
dc.creator | Biscarri Triviño, Félix | es |
dc.creator | Monedero Goicoechea, Iñigo Luis | es |
dc.creator | Guerrero Alonso, Juan Ignacio | es |
dc.creator | Biscarri Triviño, Jesús | es |
dc.creator | Millán, Rocío | es |
dc.date.accessioned | 2022-03-24T09:59:12Z | |
dc.date.available | 2022-03-24T09:59:12Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | León de Mora, C., Biscarri Triviño, F., Monedero Goicoechea, I.L., Guerrero Alonso, J.I., Biscarri Triviño, J. y Millán, R. (2011). Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies. IEEE Transactions on Power Systems, 26 (4), 1798-1807. | |
dc.identifier.issn | 0885-8950 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131240 | |
dc.description.abstract | This paper proposes a comprehensive framework to
detect non-technical losses (NTLs) and recover electrical energy
(lost by abnormalities or fraud) by means of a data mining anal ysis, in the Spanish Power Electric Industry. It is divided into four
section: data selection, data preprocessing, descriptive, and pre dictive data mining. The authors insist on the importance of the
knowledge of the particular characteristics of the Power Company
customer: the main features available in databases are described.
The paper presents two innovative statistical estimators to attach
importance to variability and trend analysis of electric consump tion and offers a predictive model, based on the Generalized Rule
Induction (GRI) model. This predictive analysis discovers associa tion rules in the data and it is supplemented by a binary Quest tree
classification method. The quality of this framework is illustrated
by a case study considering a real database, supplied by Endesa
Company. | es |
dc.description.sponsorship | ENDESA TPWRS-00887-2008 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Transactions on Power Systems, 26 (4), 1798-1807. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Customer electricity consumption | es |
dc.subject | Electric fraud | es |
dc.subject | Electricity market | es |
dc.subject | Non-technical losses | es |
dc.title | Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies | es |
dc.type | info:eu-repo/semantics/article | 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.projectID | TPWRS-00887-2008 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/5738710 | es |
dc.identifier.doi | 10.1109/TPWRS.2011.2121350 | es |
dc.journaltitle | IEEE Transactions on Power Systems | es |
dc.publication.volumen | 26 | es |
dc.publication.issue | 4 | es |
dc.publication.initialPage | 1798 | es |
dc.publication.endPage | 1807 | es |
dc.contributor.funder | ENDESA | es |