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Demonstration of an Energy Consumption Forecasting System for Energy Management in Buildings

2019-08Journal article Open access
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dc.contributor.authorJozi, Aria
dc.contributor.authorRamos, Daniel
dc.contributor.authorGomes, Luis
dc.contributor.authorFaria, Pedro
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-05T10:18:21Z
dc.date.available2021-02-05T10:18:21Z
dc.date.issued2019-08
dc.description.abstractDue to the increment of the energy consumption and dependency of the nowadays lifestyle to the electrical appliances, the essential role of an energy management system in the buildings is realized more than ever. With this motivation, predicting energy consumption is very relevant to support the energy management in buildings. In this paper, the use of an energy management system supported by forecasting models applied to energy consumption prediction is demonstrated. The real-time automatic forecasting system is running separately but integrated with the existing SCADA system. Nine different forecasting approaches to obtain the most reliable estimated energy consumption of the building during the following hours are implemented.pt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of the following projects: European Union's Horizon 2020 project DOMINOES (grant agreement No 771066), COLORS Project PTDC/EEI-EEE/28967/2017 and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-30241-2_39pt_PT
dc.identifier.isbn978-3-030-30240-5
dc.identifier.urihttp://hdl.handle.net/10400.22/16890
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-30241-2_39pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectEnergy consumptionpt_PT
dc.subjectEnergy management systempt_PT
dc.subjectForecastpt_PT
dc.titleDemonstration of an Energy Consumption Forecasting System for Energy Management in Buildingspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.citation.conferencePlaceVila Real, Portugalpt_PT
oaire.citation.endPage468pt_PT
oaire.citation.startPage462pt_PT
oaire.citation.titleEPIA Conference on Artificial Intelligence (EPIA 2019)pt_PT
oaire.citation.volume11804pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameJozi
person.familyNameFaria
person.familyNamePinto
person.familyNameVale
person.givenNameAria
person.givenNamePedro
person.givenNameTiago
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
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person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-0968-7879
person.identifier.orcid0000-0002-8597-3383
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id57193337928
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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