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Electricity consumption forecasting in office buildings: an artificial intelligence approach

2019Conference object Open access
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dc.contributor.authorJozi, Aria
dc.contributor.authorPinto, Tiago
dc.contributor.authorMarreiros, Goreti
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-05T10:22:22Z
dc.date.available2021-02-05T10:22:22Z
dc.date.issued2019
dc.description.abstractThe rising needs for increased energy efficiency and better use of renewable energy sources bring out the necessity for improved energy management and forecasting models. Electricity consumption, in particular, is subject to large variations due to the effect of multiple variables, such as the temperature, luminosity or humidity, and of course, consumers' habits. Current forecasting models are not able to deal adequately with the influence and correlation between the multiple involved variables. Hence, novel, adaptive forecasting models are needed. This paper presents a novel approach based on multiple artificial intelligence-based forecasting algorithms. The considered algorithms are artificial neural networks, support vector machines hybrid fuzzy inference systems, Wang and Mendel's fuzzy rule learning method and a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology. These algorithms are used to forecast the electricity consumption of a real office building, using multiple input variables and consumption disaggregation.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/PTC.2019.8810503pt_PT
dc.identifier.isbn978-1-5386-4722-6
dc.identifier.urihttp://hdl.handle.net/10400.22/16891
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8810503pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectElectricity consumptionpt_PT
dc.subjectForecastingpt_PT
dc.subjectOffice buildingpt_PT
dc.subjectSupport vector machinespt_PT
dc.subjectPredictive modelspt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectAdaptation modelspt_PT
dc.titleElectricity consumption forecasting in office buildings: an artificial intelligence approachpt_PT
dc.typeconference object
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.conferencePlaceMilan, Italypt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2019 IEEE Milan PowerTechpt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameJozi
person.familyNamePinto
person.familyNameMarreiros
person.familyNameVale
person.givenNameAria
person.givenNameTiago
person.givenNameGoreti
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idA412-138E-2389
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-0968-7879
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0003-4417-8401
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridM-4583-2013
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id57193337928
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id9332465700
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.typeconferenceObjectpt_PT
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