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Ensemble learning for electricity consumption forecasting in office buildings

2021Journal article Open access
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dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.contributor.authorSilva, Jose
dc.date.accessioned2021-09-22T10:15:49Z
dc.date.available2023-05-31T00:32:31Z
dc.date.issued2021
dc.description.abstractThis paper presents three ensemble learning models for short term load forecasting. Machine learning has evolved quickly in recent years, leading to novel and advanced models that are improving the forecasting results in multiple fields. However, in highly dynamic fields such as power and energy systems, dealing with the fast acquisition of large amounts of data from multiple data sources and taking advantage from the correlation between the multiple available variables is a challenging task, for which current models are not prepared. Ensemble learning is bringing promising results in this sense, as, by combining the results and use of multiple learners, is able to find new ways for current learning models to be used and optimized. In this paper three ensemble learning models are developed and the respective results compared: gradient boosted regression trees, random forests and an adaptation of Adaboost. Results for electricity consumption forecasting in hour-ahead are presented using a case-study based on real data from an office building. Results show that the adapted Adaboost model outperforms the reference models for hour-ahead load forecasting.pt_PT
dc.description.sponsorshipThis work has been developed under the SPET project - PTDC/EEI-EEE/29165/2017 and has received funding from UID/EEA/00760/2019, funded by FEDER Funds through COMPETE andby National Funds through FCTpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.neucom.2020.02.124pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18463
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0925231220307372?via%3Dihubpt_PT
dc.subjectEnergy consumptionpt_PT
dc.subjectEnsemble learningpt_PT
dc.subjectMachine learningpt_PT
dc.subjectLoad forecastingpt_PT
dc.titleEnsemble learning for electricity consumption forecasting in office buildingspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-EEE%2F29165%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.citation.endPage755pt_PT
oaire.citation.startPage747pt_PT
oaire.citation.titleNeurocomputingpt_PT
oaire.citation.volume423pt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
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person.familyNamePraça
person.familyNameVale
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
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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.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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