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dc.contributor.authorMor Martínez, Gerard
dc.contributor.authorCipriano, Jordi
dc.contributor.authorMartirano, Giacomo
dc.contributor.authorPignatelli, Francesco
dc.contributor.authorLodi, Chiara
dc.contributor.authorLazzari, Florencia
dc.contributor.authorGrillone, Benedetto
dc.contributor.authorChemisana Villegas, Daniel
dc.date.accessioned2021-10-14T10:25:33Z
dc.date.available2021-10-14T10:25:33Z
dc.date.issued2021
dc.identifier.issn2352-4847
dc.identifier.urihttp://hdl.handle.net/10459.1/72061
dc.description.abstractA bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper’s novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector.ca_ES
dc.description.sponsorshipThis work emanated from research conducted with the fi-nancial support of the European Commission through the H2020project BIGG , grant agreement 957047, and the JRC Expert Con-tractCT-EX2017D306558-102.D.ChemisanathanksICREAfortheICREA Acadèmia. Dr J. Cipriano also thanks the Ministerio deCiencia e Innovación of the Spanish Government for the Juan dela Cierva Incorporación grantca_ES
dc.language.isoengca_ES
dc.publisherElsevierca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1016/j.egyr.2021.08.195ca_ES
dc.relation.ispartofEnergy Reports, 2021, vol. 7, p. 5667-5684ca_ES
dc.rightscc-by (c) Mor et al., 2021ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBuilding-stockmodelsca_ES
dc.subjectElectricityca_ES
dc.subjectCharacterisationca_ES
dc.subjectData-drivenca_ES
dc.titleA data-driven method for unsupervised electricity consumption characterisation at the district level and beyondca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1016/j.egyr.2021.08.195
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/957047/EU/BIGGca_ES


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cc-by (c) Mor et al., 2021
Except where otherwise noted, this item's license is described as cc-by (c) Mor et al., 2021