Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment

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dc.contributor.authorKrč, Rostislavcs
dc.contributor.authorFloriánová, Martinacs
dc.contributor.authorPodroužek, Jancs
dc.contributor.authorApeltauer, Tomášcs
dc.contributor.authorStupka, Václavcs
dc.contributor.authorPitner, Tomášcs
dc.coverage.issue5cs
dc.coverage.volume13cs
dc.date.issued2021-03-09cs
dc.description.abstractAs energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged.en
dc.formattextcs
dc.format.extent1-18cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSustainability. 2021, vol. 13, issue 5, p. 1-18.en
dc.identifier.doi10.3390/su13052954cs
dc.identifier.issn2071-1050cs
dc.identifier.orcid0000-0001-6772-2575cs
dc.identifier.orcid0000-0002-8001-6349cs
dc.identifier.orcid0000-0003-0493-5922cs
dc.identifier.orcid0000-0003-3186-2175cs
dc.identifier.other170530cs
dc.identifier.researcheridQ-2414-2015cs
dc.identifier.scopus57205731839cs
dc.identifier.scopus25121877100cs
dc.identifier.scopus35067734400cs
dc.identifier.urihttp://hdl.handle.net/11012/200892
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSustainabilitycs
dc.relation.urihttps://www.mdpi.com/2071-1050/13/5/2954/pdfcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2071-1050/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectsmart griden
dc.subjectelectricity networken
dc.subjectflexibility assessmenten
dc.subjectrenewable energy sourcesen
dc.subjectmachine learningen
dc.subjectnetwork simulationen
dc.subjectartificial neural networksen
dc.subjectconvolutional neural networksen
dc.titleMachine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessmenten
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-170530en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:44:14en
sync.item.modts2025.01.17 18:44:09en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav automatizace inženýrských úloh a informatikycs
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