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Journal of Energy in Southern Africa

On-line version ISSN 2413-3051
Print version ISSN 1021-447X

J. energy South. Afr. vol.23 n.3 Cape Town  2012


Comparing performance of MLP and RBF neural network models for predicting South Africa's energy consumption



Olanrewaju A OludolapoI; Adisa A JimohII; Pule A KholopaneIII

IDepartment of Industrial Engineering, Tshwane University of Technology, South Africa
IIDepartment of Electrical Engineering, Tshwane University of Technology, South Africa
IIIDepartment of Industrial Engineering, University of Johannesburg, South Africa




In view of the close association between energy and economic growth, South Africa's aspirations for higher growth, more energy is required; formulating a long-term economic development plan and implementing an energy strategy for a country /industry necessitates establishing the correct relationship between energy and the economy. As insufficient energy or a lack thereof is reported to be a major cause of social and economic poverty, it is very important to select a model to forecast the consumption of energy reasonably accurately. This study presents techniques based on the development of multilayer perceptron (MLP) and radial basis function (RBF) of artificial neural network (ANN) models, for calculating the energy consumption of South Africa's industrial sector between 1993 and 2000. The approach examines the energy consumption in relation to the gross domestic product. The results indicate a strong agreement between model predictions and observed values, since the mean absolute percentage error is below 5%. When performance indices are compared, the RBF-based model is a more accurate predictor than the MLP model.

Keywords: multilayer perceptron, radial basis function, energy consumption, gross domestic product



Full text available only in PDF format.




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Received 15 June 2011
Revised 7 May 2012

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