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.

• Periodical: Advanced Materials Research (Volumes 217-218)
• Pages: 39-44
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.217-218.39
• Online since: March 2011
• Authors: Alessandra Bonato Altran, Carlos Roberto Minussi, Mara Lúcia Marthins Lopes, Fábio Roverto Chavarette, Nelson José Peruzzi
• Keywords: Artificial Neural Network (ANN), Backpropagation Algorithm, Multinodal Forecast of Electric Load, Radial Basis Function (RBF)

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