Please use this identifier to cite or link to this item:
Title: NeuroDem - a neural network based short term demand forecaster.
Authors: Silva, Alexandre Pinto Alves da
Rodrigues, Ubiratan de Paula
Reis, Agnaldo José da Rocha
Moulin, Luciano Souza
Keywords: Confidence intervals
Neural nets
Load forecasting
Issue Date: 2001
Citation: SILVA, A. P. A. da S. et al. NeuroDem - a neural network based short term temand forecaster. In. IEEE Porto Power Tech, 2001. Porto. Anais... Porto: proceedings of the IEEE porto power tech, 2001. Disponível em: <>. Acesso em: 25 jul. 2012.
Abstract: The 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.
Appears in Collections:DECAT - Trabalhos apresentados em eventos

Files in This Item:
File Description SizeFormat 
  Restricted Access
683,71 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.