Repository logo
 
Publication

Contextual learning for energy forecasting in buildings

2022Journal article Restricted access
Simple item page
dc.contributor.authorJozi, Aria
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2023-02-02T12:24:35Z
dc.date.available2023-02-02T12:24:35Z
dc.date.issued2022
dc.description.abstractEnergy consumers are becoming active players in the power and energy system. However, their informed and real-time responsiveness to the variations of renewable-based generation and, consequently, energy prices, is not possible without decision support solutions. This paper proposes a novel contextual learning approach for energy forecasting, which supports the decisions of Building Energy Management Systems (BEMS). The proposed forecasting approach includes a contextual dimension that identifies different observed contexts and clusters them according to their similarity. The identification of such contexts is used by the learning process of state-of-the-art artificial intelligence-based forecasting methods to select and adapt the most relevant data that is used in the training phase in each context. Forecasts for energy consumption, generation, temperature, brightness and occupancy are used by the BEMS to provide recommendations to the consumers and to support automated control of building devices. Real consumption, generation and contextual data gathered from several sensors in a building are used to validate the results, which show that the proposed contextual learning model improves forecasts of energy consumption, generation and other relevant factors for energy management in buildings.pt_PT
dc.description.sponsorshipThis work has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.ijepes.2021.107707pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/22113
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCEECIND/01811/2017pt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0142061521009340?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectContextual learningpt_PT
dc.subjectDemand responsept_PT
dc.subjectEnergy consumptionpt_PT
dc.subjectEnergy management systemspt_PT
dc.subjectForecastingpt_PT
dc.titleContextual learning for energy forecasting 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/UIDB%2F00760%2F2020/PT
oaire.citation.startPage107707pt_PT
oaire.citation.titleInternational Journal of Electrical Power & Energy Systemspt_PT
oaire.citation.volume136pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePinto
person.familyNameVale
person.givenNameTiago
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
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-id35219107600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isProjectOfPublicationdb3e2edb-b8af-487a-b76a-f6790ac2d86e
relation.isProjectOfPublication.latestForDiscoverydb3e2edb-b8af-487a-b76a-f6790ac2d86e

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
ART35_GECAD_ZAV_2022.pdf
Size:
1.25 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ISEP – GECAD – Artigos