NeuroDem - a neural network based short term demand forecaster

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorSilva, Alexandre Pinto Alves da-
Autor(es): dc.creatorRodrigues, Ubiratan de Paula-
Autor(es): dc.creatorReis, Agnaldo José da Rocha-
Autor(es): dc.creatorMoulin, Luciano Souza-
Data de aceite: dc.date.accessioned2019-11-06T13:24:34Z-
Data de disponibilização: dc.date.available2019-11-06T13:24:34Z-
Data de envio: dc.date.issued2012-07-25-
Data de envio: dc.date.issued2012-07-25-
Data de envio: dc.date.issued2001-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/123456789/1208-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/554769-
Descrição: dc.descriptionThe application of Neural Network (NN) based Short-Term Load Forecasting (STLF) has developed to sophisticated practical systems over the years. However, the question of how to maximize the generalization ability of such machines, together with the choice of architecture, activation functions, training set data and size, etc. makes up a huge number of possible combinations for the final NN design, whose optimal solution has not been figured yet. This paper describes a STLF system (NeuroDem) which has been used by Brazilian electric utilities for 3 years. It uses a non-parametric model based on a linear model coupled with a polynomial network, identified by pruninglgrowing mechanisms. NeuroDem has special features of data pre-processing and confidence intervals calculations, which are also described. Results of load forecasts are presented for one year with forecasting horizons from 15 min. to 168 hours ahead.-
Idioma: dc.languageen-
Palavras-chave: dc.subjectConfidence intervals-
Palavras-chave: dc.subjectNeural nets-
Palavras-chave: dc.subjectLoad forecasting-
Título: dc.titleNeuroDem - a neural network based short term demand forecaster-
Aparece nas coleções:Repositório Institucional - UFOP

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