Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

• Autor(a) principal: Altran, Alessandra Bonato [UNESP]
• Data de Publicação: 2011
• Outros Autores: Minussi, Carlos Roberto [UNESP], Martins Lopes, Mara Lucia, Chavarette, Fábio Roberto [UNESP], Peruzzi, Nelson Jose
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.4028/www.scientific.net/AMR.217-218.39; http://hdl.handle.net/11449/9787
• topic: Multinodal Forecast of Electric Load; Artificial Neural Networks; Backpropagation Algorithm; Radial Basis Function

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.