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Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study

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dc.contributor.authorJozi, Aria
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorRamos, Sérgio
dc.contributor.authorVale, Zita
dc.contributor.authorGoujon, Benedicte
dc.contributor.authorPetrisor, Teodora
dc.date.accessioned2021-03-09T10:51:11Z
dc.date.available2021-03-09T10:51:11Z
dc.date.issued2017
dc.description.abstractElectrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.pt_PT
dc.description.sponsorshipThis work has been developed under the EUREKA - ITEA2 Project FUSE-IT (ITEA-13023), Project GREEDI (ANI|P2020 17822), and has received funding 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/SSCI.2017.8285200pt_PT
dc.identifier.isbn978-1-5386-2726-6
dc.identifier.urihttp://hdl.handle.net/10400.22/17318
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationITEA-13023pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8285200pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectEnergy consumptionpt_PT
dc.subjectForecastingpt_PT
dc.subjectFuzzy rule-based systemspt_PT
dc.subjectTRT buildingpt_PT
dc.titleEnergy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case studypt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceHonolulu, HI, USApt_PT
oaire.citation.endPage5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2017 IEEE Symposium Series on Computational Intelligence (SSCI)pt_PT
oaire.fundingStream5876
person.familyNameJozi
person.familyNamePinto
person.familyNamePraça
person.familyNameCarvalho Ramos
person.familyNameVale
person.givenNameAria
person.givenNameTiago
person.givenNameIsabel
person.givenNameSérgio Filipe
person.givenNameZita
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
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person.identifier.orcid0000-0002-0968-7879
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-1120-5656
person.identifier.orcid0000-0002-4560-9544
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
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
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