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

被引：2

作者：

Altran, Alessandra Bonato ^{[1
]}

Minussi, Carlos Roberto ^{[1
]}

Martins Lopes, Mara Lucia ^{[2
]}

Chavarette, Fabio Roberto ^{[2
]}

Peruzzi, Nelson Jose ^{[3
]}

机构：

[1] UNESP Univ Estadual Paulista, Fac Engn, Dept Elect Engn, Ave Jose Carlos Rossi 1370, BR-15385000 Ilha Solteira, SP, Brazil

[2] UNESP, Fac Engn, Dept Math, BR-15385000 Avenida, SP, Brazil

[3] UNESP, Dept Exact Sci, BR-13484900 Avenida, SP, Brazil

来源：

HIGH PERFORMANCE STRUCTURES AND MATERIALS ENGINEERING, PTS 1 AND 2 | 2011年 / 217-218卷

关键词：

Multinodal Forecast of Electric Load; Artificial Neural Networks; Backpropagation Algorithm; Radial Basis Function;

D O I：

10.4028/www.scientific.net/AMR.217-218.39

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

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.

引用

页码：39 / +

页数：2

共 11 条

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