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Home energy forecast performance tool for smart living services suppliers under an energy 4.0 and CPS framework

2022-01-28Journal article Open access
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dc.contributor.authorRODRIGUES, FILIPE
dc.contributor.authorCardeira, Carlos
dc.contributor.authorCalado, João Manuel Ferreira
dc.contributor.authorMelicio, Rui
dc.date.accessioned2022-02-16T14:50:34Z
dc.date.available2022-02-16T14:50:34Z
dc.date.issued2022-01-28
dc.description.abstractIndustry 4.0 is a paradigm consisting of cyber-physical systems based on the interconnection between all sorts of machines, sensors, and actuators, generally known as things. The combination of energy technology and information and technology communication (ICT) enables measure ment, control, and automation to be performed across the distributed grid with high time resolution. Through digital revolution in the energy sector, the term Energy 4.0 emerges in the future electric sector. The growth outlook for appliance usage is increasing and the appearance of renewable energy sources on the electric grid requires strategies to control demand and peak loads. Potential feedback for energy performance is the use of smart meters in conjunction with smart energy man agement; well-designed applications will successfully inform, engage, empower, and motivate con sumers. This paper presents several hands-on tools for load forecasting, comparing previous works and verifying which show the best energy forecasting performance in a smart monitoring system. Simulations were performed based on forecasting of the hours ahead of the load for several households. Special attention was given to the accuracy of the forecasting model for weekdays and weekends. The development of the proposed methods, based on artificial neural networks (ANN), pro vides more reliable forecasting for a few hours ahead and peak loads.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRODRIGUES, Filipe Martins; [et al] – Home energy forecast performance tool for smart living services suppliers under an energy 4.0 and CPS framework. Energies. eISSN: 1996-1073. Vol. 15, N.º 3 (2022), pp. 1-21.pt_PT
dc.identifier.doi10.3390/en15030957pt_PT
dc.identifier.eissn1996-1073
dc.identifier.urihttp://hdl.handle.net/10400.21/14302
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.relationUIDB/50022/2020 - FCT, through IDMEC, under LAETApt_PT
dc.relationUIDB/04683/2020 - FCT under the ICT (Institute of Earth Sciences)pt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/15/3/957pt_PT
dc.subjectIndustry 4.0pt_PT
dc.subjectEnergy managementpt_PT
dc.subjectSmart gridspt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectSmart homept_PT
dc.subjectSmart meterpt_PT
dc.subjectForecastingpt_PT
dc.titleHome energy forecast performance tool for smart living services suppliers under an energy 4.0 and CPS frameworkpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage21pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume15pt_PT
person.familyNameMARTINS RODRIGUES
person.familyNameCardeira
person.familyNameCalado
person.familyNameMelicio
person.givenNameFILIPE
person.givenNameCarlos
person.givenNameJoão
person.givenNameRui
person.identifierhttps://scholar.google.com/citations?user=ToPp48IAAAAJ&hl=pt-PT
person.identifier1080709
person.identifier370725
person.identifier.ciencia-idF614-709A-B61D
person.identifier.ciencia-id961C-A2E8-61CE
person.identifier.ciencia-idB518-93E3-E7AB
person.identifier.ciencia-idA213-8E2D-0102
person.identifier.orcid0000-0001-7268-5908
person.identifier.orcid0000-0002-7966-4648
person.identifier.orcid0000-0001-6628-4657
person.identifier.orcid0000-0002-1081-2729
person.identifier.ridK-9502-2013
person.identifier.ridM-4167-2013
person.identifier.ridM-4593-2013
person.identifier.scopus-author-id6507855823
person.identifier.scopus-author-id7006897277
person.identifier.scopus-author-id20436053900
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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