A regression model to assess the social acceptance of demand response programs

dc.contributor.authorFerreira, Paula Varandaspor
dc.contributor.authorRocha, Ana Isabel Carneiropor
dc.contributor.authorAraújo, Maria Madalena Teixeira depor
dc.date.accessioned2024-04-03T07:21:08Z
dc.date.available2024-04-03T07:21:08Z
dc.date.issued2021-04
dc.date.updated2024-03-28T12:19:49Z
dc.description.abstractResidential demand response has been playing an important role in the low carbon energy system transition. Although this is not a new concept, the popularity of Demand Response (DR) programs is growing, driven by the increasing opportunities that emerged with smart grid appliances as well as by their potential to support the integration of variable renewables generation. The end-user plays a key role in the successful deployment and dissemination of these DR programs. This study aims to assess social awareness and acceptance of DR programs, based on a survey for data collection and complemented with the regression models. The results suggest that the economic determinants, contribution to environmental protection as well as the extent of urbanization are important motivating drivers, to be explored in the future to encourage the residential consumers’ participation in DR programs.por
dc.description.sponsorshipThis work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT Fundação para a Ciência e a Tecnologia, within project SAICTPAC/0004/2015-POCI/01/0145/FEDER/016434.as well as by the ALGORITMI research Centre within the R&D Units Project Scope: UIDB/00319/2020por
dc.distributioninternationalpor
dc.identifier.doi10.1007/978-3-030-73585-2_6por
dc.identifier.eisbn978-3-030-73585-2
dc.identifier.eissn1867-822X
dc.identifier.isbn978-3-030-73584-5
dc.identifier.issn1867-8211
dc.identifier.urihttps://hdl.handle.net/1822/90456
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.relationSAICTPAC/0004/2015-POCI/01/0145/FEDER/016434por
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PTpor
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-73585-2_6por
dc.rightsopenAccesspor
dc.subjectDemand responsepor
dc.subjectHeterogeneous choice model (oglm)por
dc.subjectOrdered logit regression (ologit)por
dc.subjectResidential consumerspor
dc.subjectSocial acceptancepor
dc.titleA regression model to assess the social acceptance of demand response programspor
dc.typeconferencePaperpor
dspace.entity.typePublicationen
oaire.citationEndPage93por
oaire.citationStartPage84por
oaire.citationVolume375por
sdum.bookTitleSustainable Energy for Smart Citiespor
sdum.export.identifier14888
sdum.journalLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST)por

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