Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function
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Undergraduate course
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Trans Tech Publications Ltd
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Work presented at event
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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.
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Keywords
Multinodal Forecast of Electric Load, Artificial Neural Networks, Backpropagation Algorithm, Radial Basis Function
Language
English
Citation
High Performance Structures and Materials Engineering, Pts 1 and 2. Stafa-zurich: Trans Tech Publications Ltd, v. 217-218, p. 39-44, 2011.
