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Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

2019Journal article Open access
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dc.contributor.authorJozi, Aria
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorSilva, Francisco
dc.contributor.authorTeixeira, Brígida
dc.contributor.authorVale, Zita
dc.date.accessioned2021-01-28T15:51:15Z
dc.date.available2021-01-28T15:51:15Z
dc.date.issued2019
dc.description.abstractThis paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologiespt_PT
dc.description.sponsorshipThis work has been developed under the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.14201/ADCAIJ2019815564pt_PT
dc.identifier.issn2255-2863
dc.identifier.urihttp://hdl.handle.net/10400.22/16783
dc.language.isoengpt_PT
dc.publisherEdiciones Universidad de Salamancapt_PT
dc.relation.publisherversionhttps://revistas.usal.es//index.php/2255-2863/article/view/ADCAIJ2019815564pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectElectricity consumptionpt_PT
dc.subjectForecastingpt_PT
dc.subjectFuzzy rule based methodspt_PT
dc.subjectMOGULpt_PT
dc.titleGenetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecastingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage64pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage55pt_PT
oaire.citation.titleADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNALpt_PT
oaire.citation.volume8pt_PT
person.familyNameJozi
person.familyNamePraça
person.familyNameSilva
person.familyNameTeixeira
person.familyNameVale
person.givenNameAria
person.givenNameIsabel
person.givenNameFrancisco
person.givenNameBrígida
person.givenNameZita
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person.identifier.scopus-author-id57193337928
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id56870827300
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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