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dc.creatorLuque Rodríguez, Joaquínes
dc.creatorCarrasco Muñoz, Alejandroes
dc.creatorPersonal Vázquez, Enriquees
dc.creatorPérez García, Franciscoes
dc.creatorLeón de Mora, Carloses
dc.date.accessioned2024-01-03T09:12:17Z
dc.date.available2024-01-03T09:12:17Z
dc.date.issued2023-01
dc.identifier.citationLuque Rodríguez, J., Carrasco Muñoz, A., Personal Vázquez, E., Pérez García, F. y León de Mora, C. (2023). Customer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locations. IEEE Transactions on Power Systems, 39 (1), 2010-2019. https://doi.org/10.1109/TPWRS.2023.3239635.
dc.identifier.issn0885-8950es
dc.identifier.issn1558-0679es
dc.identifier.urihttps://hdl.handle.net/11441/152891
dc.description.abstractThe increasing competition in the electric sector is challenging retail companies as they must assign its commercial efforts to attract the most profitable customers. Those are whose energy demand best fit certain target profiles, which usually depend on generation or cost policies. But, even when the demand profile is available, it is in an anonymous way, preventing its association to a particular client. In this paper, we explore a large dataset containing several millions of monthly demand profiles in Spain and use the available information about the associated economic sector and location for an indirect identification of the customers. The distance of the demand profile from the target is used to define a key performance indicator (KPI) which is used as the main driver of the proposed marketing strategy. The combined use of activity and location has been revealed as a powerful tool for indirect identification of customers, as 100,000 customers are uniquely identified, while about 300,000 clients are identifiable in small sets containing 10 or less consumers. To assess the proposed marketing strategy, it has been compared to the random attraction of new clients, showing a reduction of distance from the target of 40% for 10,000 new customers.es
dc.formatapplication/pdfes
dc.format.extent10 p.es
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relation.ispartofIEEE Transactions on Power Systems, 39 (1), 2010-2019.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPower systemses
dc.subjectElectricity marketses
dc.subjectLoad profilees
dc.subjectMarketing strategyes
dc.subjectEconomic sectores
dc.titleCustomer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locationses
dc.typeinfo:eu-repo/semantics/articlees
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.projectIDRTI2018-094917-B-I00es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/10025587es
dc.identifier.doi10.1109/TPWRS.2023.3239635es
dc.contributor.groupUniversidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industriales
idus.validador.notaPreprint. Submitted versiones
dc.journaltitleIEEE Transactions on Power Systemses
dc.publication.volumen39es
dc.publication.issue1es
dc.publication.initialPage2010es
dc.publication.endPage2019es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades, Government of Spain under the project “Bigdata Analitycs e Instrumentación Cyberfísica para Soporte de Operaciones de Distribución en la Smart Grid”, number RTI2018-094917-B-I00es

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