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Design of ensemble forecasting models for home energy management systems

2021-11-16Journal article Open access
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dc.contributor.authorBot, Karol
dc.contributor.authorSantos, Samira
dc.contributor.authorHabou Laouali, Inoussa
dc.contributor.authorRuano, Antonio
dc.contributor.authorRuano, Maria da Graça
dc.date.accessioned2021-12-13T16:11:28Z
dc.date.available2021-12-13T16:11:28Z
dc.date.issued2021-11-16
dc.date.updated2021-11-25T16:00:13Z
dc.description.abstractThe increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEnergies 14 (22): 7664 (2021)pt_PT
dc.identifier.doi10.3390/en14227664pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.1/17385
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEnergy systemspt_PT
dc.subjectMachine learningpt_PT
dc.subjectForecastingpt_PT
dc.subjectEnergy management systemspt_PT
dc.subjectMulti-objective genetic algorithmspt_PT
dc.subjectEnsemble modelspt_PT
dc.subjectEnergy in buildingspt_PT
dc.titleDesign of ensemble forecasting models for home energy management systemspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.citation.issue22pt_PT
oaire.citation.startPage7664pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume14pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameHABOU LAOUALI
person.familyNameRuano
person.familyNameRuano
person.givenNameInoussa
person.givenNameAntonio
person.givenNameMaria
person.identifier.ciencia-id3B19-9F1C-E2F1
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0001-5904-2166
person.identifier.orcid0000-0002-6078-6813
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0002-0014-9257
person.identifier.ridB-4135-2008
person.identifier.ridA-8321-2011
person.identifier.scopus-author-id7004284159
person.identifier.scopus-author-id7004483805
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationba2eedb0-4eca-4346-a332-969d82e740a4
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2
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