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Electricity Consumption Forecast in an Industry Facility to Support Production Planning Update in Short Time

2020Conference object Restricted access
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dc.contributor.authorRamos, Daniel
dc.contributor.authorFaria, Pedro
dc.contributor.authorVale, Zita
dc.contributor.authorCorreia, Regina
dc.date.accessioned2021-03-03T17:46:56Z
dc.date.issued2020
dc.description.abstractThe global environmental concerns raise the need to decrease energy, namely electricity consumption. Energy consumption can be reduced by improving energy efficiency and by improving the optimization of energy management in each context. These opportunities are very relevant in buildings and industry facilities. In order to improve the optimized energy management, adequate forecasting tools are needed regarding the load consumption patterns in each building. In the present paper, two forecasting technics, namely neural networks, and support vector machine, are used to predict the consumption of an industry facility for each 5 minutes. The proposed model finds the best method in order to be used in a later stage regarding the updated of production planning. The size of historic data is also discussed. The case study includes one-week test data and more than one-year train datapt_PT
dc.description.sponsorshipThis work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224), the scope of ITEA3 project SPEAR 16001 and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/EEEIC/ICPSEurope49358.2020.9160535pt_PT
dc.identifier.isbn978-1-7281-7455-6
dc.identifier.urihttp://hdl.handle.net/10400.22/17268
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9160535pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectIndustry facilitypt_PT
dc.subjectLoad forecastpt_PT
dc.subjectProduction planningpt_PT
dc.titleElectricity Consumption Forecast in an Industry Facility to Support Production Planning Update in Short Timept_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT
oaire.citation.conferencePlaceMadrid, Spainpt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFaria
person.familyNameVale
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
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
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