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Day ahead electricity consumption forecasting with MOGUL learning model

2018Conference object Open access
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dc.contributor.authorJozi, Aria
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.contributor.authorSoares, João
dc.date.accessioned2021-02-19T16:47:38Z
dc.date.available2021-02-19T16:47:38Z
dc.date.issued2018
dc.description.abstractDue to amount of today's electricity consumption, one of the most important tasks of the energy operators is to be able to predict the consumption and be ready to control the energy generation based on the estimated consumption for the future. In this way, having a trustable forecast of the electricity consumption is essential to control the consumption and maintain the balance in energy distribution networks. This study presents a day ahead forecasting approach based on a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL). The proposed approach is used to forecast the electricity consumption of an office building in the following 24 hours. The goal of this work is to present a more reliable profile of the electricity consumption comparing to previous works. Therefore, this paper also includes the comparison of the results of day ahead forecasting using GFS.FR.MOGUL method against other fuzzy rule based methods, as well as a set of Artificial Neural Network(ANN) approaches. This comparison shows that using the GFS.FR.MOGUL forecasting method for day-ahead electricity consumption forecasting is able to estimate a more trustable value than the other approaches.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/IJCNN.2018.8489134pt_PT
dc.identifier.isbn978-1-5090-6014-6
dc.identifier.issn2161-4407
dc.identifier.urihttp://hdl.handle.net/10400.22/17056
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8489134pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectDay-ahead forecastingpt_PT
dc.subjectElectricity consumptionpt_PT
dc.subjectMOGUL learning methodologypt_PT
dc.subjectOffice buildingpt_PT
dc.subjectMOGUL learning modelpt_PT
dc.subjectEnergy distribution networkspt_PT
dc.titleDay ahead electricity consumption forecasting with MOGUL learning modelpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceRio de Janeiro, Brazilpt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2018 International Joint Conference on Neural Networks (IJCNN)pt_PT
oaire.fundingStream5876
person.familyNameJozi
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.familyNameSoares
person.givenNameAria
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
person.givenNameJoão
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
person.identifier1043580
person.identifier.ciencia-id2414-9B03-C4BB
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person.identifier.orcid0000-0002-0968-7879
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0002-4172-4502
person.identifier.ridT-2245-2018
person.identifier.ridK-8430-2014
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id57193337928
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id35436109600
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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