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Comparing forecasting performances between multilayer feedforward neural network and recurrent neural network in Malaysia's load

Mohamed, Norizan and Ahmad, Maizah Hura and Ismail, Zuhaimy and Arshad, Khairil Anuar (2010) Comparing forecasting performances between multilayer feedforward neural network and recurrent neural network in Malaysia's load. Journal of Interdisciplinary Mathematics, 13 (2). pp. 125-134. ISSN 0972-0502

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Abstract

This paper presents the use of two artificial neural networks models, namely the multilayer feedforward neural network (MLFF) and the recurrent neural network (RNN) are applied for Malaysia’s load forecasting. For this purpose, a half hourly load data is divided equally into three distinct sets for training, validation and testing. We use backpropagation as the learning algorithm and the sigmoid function as the transfer function for both hidden land output layers. The forecasting performances of were compared between these two models. We use the sum squared error (SSE) as the measure of performance and the correlation coef?cient r , as the measure of relationship between the actual and the predicted values. Results show that, multilayer feedforward neural network (MLFF) and recurrent neural network (RNN) have comparable accuracy but the sum squared error for multilayer feedforward neural network (MLFF) is lower, thus making it better model than recurrent neural network (RNN).

Item Type:Article
Uncontrolled Keywords:load forecasting, artificial neural networks, multilayer feedforward neural network, recurrent neural network
Subjects:Q Science > Q Science (General)
Divisions:Science
ID Code:25936
Deposited By: Liza Porijo
Deposited On:18 Jun 2012 02:53
Last Modified:18 Jun 2012 02:53

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