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Day-ahead forecasting approach for energy consumption of an office building using support vector machines

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dc.contributor.authorJozi, Aria
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-03T16:47:56Z
dc.date.available2021-02-03T16:47:56Z
dc.date.issued2018
dc.description.abstractThis paper presents a Support Vector Machine (SVM) based approach for energy consumption forecasting. The proposed approach includes the combination of both the historic log of past consumption data and the history of contextual information. By combining variables that influence the electrical energy consumption, such as the temperature, luminosity, seasonality, with the log of consumption data, it is possible for the proposed method by find patterns and correlations between the different sources of data and therefore improves the forecasting performance. A case study based on real data from a pilot microgrid located at the GECAD campus in the Polytechnic of Porto is presented. Data from the pilot buildings are used, and the results are compared to those achieved by several states of the art forecasting approaches. Results show that the proposed method can reach lower forecasting errors than the other considered methods.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and 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.2018.8628734pt_PT
dc.identifier.isbn978-1-5386-9276-9
dc.identifier.urihttp://hdl.handle.net/10400.22/16862
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8628734pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectSupport Vector Machine (SVM)pt_PT
dc.subjectData series analysispt_PT
dc.subjectEnergy consumption forecastingpt_PT
dc.subjectOffice buildingpt_PT
dc.titleDay-ahead forecasting approach for energy consumption of an office building using support vector machinespt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceBangalore, Indiapt_PT
oaire.citation.endPage1625pt_PT
oaire.citation.startPage1620pt_PT
oaire.citation.title2018 IEEE Symposium Series on Computational Intelligence (SSCI)pt_PT
oaire.fundingStream5876
person.familyNameJozi
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.givenNameAria
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.orcid0000-0002-0968-7879
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-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
rcaap.typeconferenceObjectpt_PT
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ISEP – GECAD – Comunicações em eventos científicos