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dc.creatorLuque Rodríguez, Joaquínes
dc.creatorPersonal Vázquez, Enriquees
dc.creatorGarcía Delgado, Antonioes
dc.creatorLeón de Mora, Carloses
dc.date.accessioned2021-06-28T12:55:01Z
dc.date.available2021-06-28T12:55:01Z
dc.date.issued2021-06
dc.identifier.citationLuque Rodríguez, J., Personal Vázquez, E., García Delgado, A. y León de Mora, C. (2021). Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location. IEEE Access, 9, 86254-86267.
dc.identifier.issn2169-3536es
dc.identifier.urihttps://hdl.handle.net/11441/114907
dc.description.abstractIn a highly competitive and liberalized energy market, where the retail of electricity is open to many potential companies, it is essential to have tools that help make decisions and guide the design of marketing strategies. In this sense, it is essential for retailers to know the behavior of their customers to correctly define their commercial strategies. One of the most commonly used methods for this is the characterization of their consumption profiles. Fortunately, for regulatory reasons, in some countries, the monthly electricity demand of each customer is openly available to any competitor. This paper explores whether this information, especially the economic sector and geographic location of a client, is useful for determining the client’s demand profile. Specifically, data on electricity demand in Spain from more than 27 million users and for a period of 3 years are analyzed. For this purpose, the electricity consumption of every client is grouped by month and normalized. The resulting demand profiles are later clustered according to different criteria. The main finding of the research is that the combined information on economic activity and location definitely enables prediction of the demand profile. Additionally, profile quality metrics are defined and obtained for the entire dataset. The resulting profiles have a mean dispersion of 10% and a confidence interval of ±17%. To clarify the use of these metrics, several examples are detailed, showing how this profile information can be used to improve the marketing decision-making process for electricity retailers.es
dc.description.sponsorshipFondo Europeo de Desarrollo Regional (FEDER)/Ministerio de Ciencia e Innovación. Agencia Estatal de Investigación, Government of Spain, Project RTI2018-094917-B-100es
dc.description.sponsorshipCentre for the Development of Industrial Technology (CDTI), Government of Spain, Project Eternal Energyes
dc.formatapplication/pdfes
dc.format.extent14 p.es
dc.language.isoenges
dc.publisherIEEEes
dc.relation.ispartofIEEE Access, 9, 86254-86267.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEnergy demandes
dc.subjectCustomer profilinges
dc.subjectData engineeringes
dc.subjectBig data applicationses
dc.subjectStatistical learninges
dc.subjectPattern analysises
dc.titleMonthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Locationes
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Tecnología Electrónicaes
dc.relation.projectIDRTI2018-094917-B-100es
dc.relation.projectIDEternal Energyes
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/9455417es
dc.identifier.doi10.1109/ACCESS.2021.3089443es
dc.contributor.groupUniversidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industriales
dc.journaltitleIEEE Accesses
dc.publication.volumen9es
dc.publication.initialPage86254es
dc.publication.endPage86267es

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