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Energy consumption forecasting using genetic fuzzy rule-based systems based on MOGUL learning methodology

2017Conference object 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.accessioned2021-03-09T14:42:03Z
dc.date.available2021-03-09T14:42:03Z
dc.date.issued2017
dc.description.abstractOne of the most challenging tasks for energy domain stakeholders is to have a better preview of the electricity consumption. Having a more trustable expectation of electricity consumption can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study using a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) methodology in order to have a better profile of the electricity consumption of the following hours. The proposed approach uses the electricity consumption of the past hours to forecast the consumption value for the following hours. Results from this study are compared to those of previous approaches, namely two fuzzy based systems: and several different approaches based on artificial neural networks. The comparison of the achieved results with those achieved by the previous approaches shows that this approach can calculate a more reliable value for the electricity consumption in the following hours, as it is able to achieve lower forecasting errors, and a less standard deviation of the forecasting error resultspt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of the following projects: European Union's Horizon 2020 research and innovation programme, under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT); 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/PTC.2017.7981219pt_PT
dc.identifier.isbn978-1-5090-4237-1
dc.identifier.urihttp://hdl.handle.net/10400.22/17335
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationAdaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/7981219pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectElectricity consumptionpt_PT
dc.subjectForecastingpt_PT
dc.subjectFuzzy rule based methodspt_PT
dc.subjectMOGULpt_PT
dc.titleEnergy consumption forecasting using genetic fuzzy rule-based systems based on MOGUL learning methodologypt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleAdaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/703689/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceManchester, UKpt_PT
oaire.citation.endPage5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleIEEE Manchester PowerTech, 2017pt_PT
oaire.fundingStreamH2020
oaire.fundingStream5876
person.familyNameJozi
person.familyNamePinto
person.familyNamePraça
person.familyNameSilva
person.familyNameTeixeira
person.familyNameVale
person.givenNameAria
person.givenNameTiago
person.givenNameIsabel
person.givenNameFrancisco
person.givenNameBrígida
person.givenNameZita
person.identifierR-000-T7J
person.identifier299522
person.identifierFWGHOEYAAAAJ
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id1B19-6E7A-A964
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-0968-7879
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0003-4551-6732
person.identifier.orcid0000-0002-0848-5319
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-id56234082600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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