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

Use este identificador para citar ou linkar para este item: http://www.repositorio.ufop.br/jspui/handle/123456789/1190

Título:	A hierarchical self-organizing map model in short-termload forecasting.
Autor(es):	Carpinteiro, Otávio Augusto Salgado Reis, Agnaldo José da Rocha
Palavras-chave:	Short-term load forecasting Self-organizing map Neural network
Data do documento:	2004
Referência:	CARPINTEIRO, O. A. S.; REIS, A. J. da R. A hierarchical self-organizing map model in short-termload forecasting. In: Congresso Brasileiro de Automática, 15., 2004. Gramado. Anais... XV Congresso Brasileiro de Automática, 2004. p.1-6. Disponível em: <http://www.lti.pcs.usp.br/robotics/grva/publicacoes/outras/cba2004-cd-rom/cba2004/pdf/548.pdf>. Acesso em: 23 jul. 2012.
Resumo:	This 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
URI:	http://www.repositorio.ufop.br/handle/123456789/1190
Aparece nas coleções:	DECAT - Trabalhos apresentados em eventos

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