Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/81025
DC FieldValueLanguage
dc.contributor.authorMiguel, Pedro-
dc.contributor.authorGonçalves, José-
dc.contributor.authorNeves, Luís-
dc.contributor.authorMartins, A.Gomes-
dc.date.accessioned2018-10-10T13:24:17Z-
dc.date.available2018-10-10T13:24:17Z-
dc.date.issued2016-
dc.identifier.issn2210-6707pt
dc.identifier.urihttp://hdl.handle.net/10316/81025-
dc.description.abstractThe objective of this work is to obtain characteristic daily profiles of consumption, wind generation and electricity spot prices, needed to develop assessments of two different options commonly regarded under the smart grid paradigm: residential demand response, and small scale distributed electric energy storage. The approach consists of applying clustering algorithms to historical data, namely using a hierarchical method and a self-organizing neural network, in order to obtain clusters of diagrams representing characteristic daily diagrams of load, wind generation or electricity price. These diagrams are useful not only to analyze different scenarios of combined existence, but also to understand their individual relative importance. This study enabled also the identification of a probable range of variation around an average profile, by defining boundary profiles with the maximum and minimum values of any cluster prototypes.pt
dc.language.isoengpt
dc.publisherElsevierpt
dc.relationCENTRO-07-0224-FEDER-002004pt
dc.relationPEst-OE/EEI/UI0308/2014pt
dc.relationUID/MULTI/00308/2013pt
dc.rightsembargoedAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectData clustering Demand response Energy box Energy storage Smart grid Distribution system operatorpt
dc.titleUsing clustering techniques to provide simulation scenarios for the smart gridpt
dc.typearticle-
degois.publication.firstPage447pt
degois.publication.lastPage455pt
degois.publication.titleSustainable Cities and Societypt
dc.peerreviewedyespt
dc.identifier.doi10.1016/j.scs.2016.04.012pt
degois.publication.volume26pt
dc.date.embargo2017-12-31*
dc.date.periodoembargo730pt
uc.date.periodoEmbargo730-
item.grantfulltextopen-
item.fulltextCom Texto completo-
Appears in Collections:I&D INESCC - Artigos em Revistas Internacionais
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