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Energy Consumption Forecasting based on Hybrid Neural Fuzzy Inference System

2016Journal 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, Brigida
dc.contributor.authorVale, Zita
dc.date.accessioned2017-07-07T13:45:05Z
dc.date.embargo2117
dc.date.issued2016
dc.description.abstractForecasting the electricity consumption is one of the most challenging tasks for energy domain stakeholders. Having reliable electricity consumption forecasts can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study regarding the forecast of electricity consumption using a methodology based on Hybrid neural Fuzzy Inference System (HyFIS). The proposed approach considers two distinct strategies, namely one strategy using only the electricity consumption as the input of the method, and the second strategy uses a combination of the electricity consumption and the environmental temperature as the input. A case study considering the forecasting of the consumption of an office building using the proposed methodologies is also presented. Results show that the second strategy is able to achieve better results, hence concluding that HyFIS is an appropriate approach to incorporate different sources of information. In this way, the environmental temperature can help the HyFIS method to achieve a more reliable forecast of the electricity consumption.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/SSCI.2016.7849859pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9999
dc.language.isoengpt_PT
dc.publisherInstitute of Electrical and Electronics Engineerspt_PT
dc.relation.ispartofseriesSSCI;2016
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7849859/pt_PT
dc.subjectElectricity Consumptionpt_PT
dc.subjectEnvironmental Temperaturept_PT
dc.subjectForecastingpt_PT
dc.subjectHybrid Neural Fuzzy Inference Systemspt_PT
dc.titleEnergy Consumption Forecasting based on Hybrid Neural Fuzzy Inference Systempt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titlePROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCEpt_PT
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
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-id35219107600
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
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relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b

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