Repository logo
 
Publication

Energy consumption forecasting based on Hybrid Neural Fuzzy Inference System

2016Conference object Open access
Simple item page
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.accessioned2021-09-23T14:12:28Z
dc.date.available2021-09-23T14:12:28Z
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.sponsorshipThe present 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.2016.7849859pt_PT
dc.identifier.isbn978-1-5090-4240-1
dc.identifier.urihttp://hdl.handle.net/10400.22/18515
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationITEA-13023pt_PT
dc.relationANI|P2020 17822pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/7849859pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/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.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceAthenas, Greecept_PT
oaire.citation.endPage5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2016 IEEE Symposium Series on Computational Intelligence (SSCI)pt_PT
oaire.fundingStream5876
person.familyNamePinto
person.familyNamePraça
person.familyNameTeixeira
person.familyNameVale
person.givenNameTiago
person.givenNameIsabel
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.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
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-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
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublicationf3d879cf-dba2-4682-af75-a2ba22130b4e
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isProjectOfPublication237af9d5-70ed-4e45-9f10-3853d860255e
relation.isProjectOfPublication.latestForDiscovery237af9d5-70ed-4e45-9f10-3853d860255e

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
COM_GECAD_CIASG_HyFIS_Jozi_2016.pdf
Size:
505.97 KB
Format:
Adobe Portable Document Format
Download

Collections

ISEP – GECAD – Comunicações em eventos científicos