A hierarchical self-organizing map model in short-termload forecasting

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorCarpinteiro, Otávio Augusto Salgado-
Autor(es): dc.creatorReis, Agnaldo José da Rocha-
Data de aceite: dc.date.accessioned2019-11-06T13:24:31Z-
Data de disponibilização: dc.date.available2019-11-06T13:24:31Z-
Data de envio: dc.date.issued2012-07-24-
Data de envio: dc.date.issued2012-07-24-
Data de envio: dc.date.issued2004-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/123456789/1190-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/554752-
Descrição: dc.descriptionThis paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets|one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them-
Idioma: dc.languageen-
Palavras-chave: dc.subjectShort-term load forecasting-
Palavras-chave: dc.subjectSelf-organizing map-
Palavras-chave: dc.subjectNeural network-
Título: dc.titleA hierarchical self-organizing map model in short-termload forecasting-
Aparece nas coleções:Repositório Institucional - UFOP

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