Paper Title:
Multinodal Load Forecasting in Power Electric Systems Using a Neural Network with Radial Basis Function
  Abstract

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

  Info
Periodical
Advanced Materials Research (Volumes 217-218)
Edited by
Zhou Mark
Pages
39-44
DOI
10.4028/www.scientific.net/AMR.217-218.39
Citation
A. Bonato Altran, C. R. Minussi, M. L. Marthins Lopes, F. R. Chavarette, N. J. Peruzzi, "Multinodal Load Forecasting in Power Electric Systems Using a Neural Network with Radial Basis Function", Advanced Materials Research, Vols. 217-218, pp. 39-44, 2011
Online since
March 2011
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Price
$35.00
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