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

 

 

Olanrewaju A Oludolapo^I; Adisa A Jimoh^II; Pule A Kholopane^III

^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

 

 

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ABSTRACT

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

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References

Al-Alawi, S., Al-Badi, A., and Ellithy, K. (2003). An artificial neural network model for predicting gas pipeline induced voltage caused by power lines under fault conditions. An artificial neural network model, 69.         [ Links ]

Amir Heydari, Shayesteh, K., and Kamalzadeh, L. (2006). Prediction of Hydrate formation temperature for natural gas using artificial neural network. Oil and Gas Business.         [ Links ]

Anonymous. (2003). Integrated Energy Plan for the Republic of South Africa. South Africa.         [ Links ]

Benghanem, M., and Mellit, A. (2010). Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia. Energy, 35, 3751-3762.         [ Links ]

Bianco, V., Manca, O., and Nardni, S. (2009). Electricity consumption forecasting in Italy using linear regresion models. Energy, 34, 1413-1421.         [ Links ]

Bishop, C. M. (1991). Improving the generalization properties of radial basis function neural networks. Neural computation, 3, 579-588.         [ Links ]

Caiqing, Z., Ruonan, Q., and Zhiwen, Q. (2008). Comparing BP and RBF Neural Network for Forecasting the Resident Consumer Level by MATLAB. International Conference on Computer and Electrical Engineering.         [ Links ]

Chatterjee, S., and Hadi, A. S., eds. (2006). Regression Analysis by Example, A John Wiley and Sons inc.         [ Links ]

Chiou-Wei, S. Z., Chen, C.-F, and Zhu, Z. (2008). Economic growth and energy consumption revisited - Evidence from linear and non-linear Granger causality. Energy Economics, 30, 3063-3076.         [ Links ]

Geem, Z. W., and Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy Policy, 37, 4049-4054.         [ Links ]

Hart, A. (1992). Using neural networks for classification tasks - some experiments on datasets and practical advice. J.Opl. Res. Soc., 43, 215 - 226.         [ Links ]

Hsu, C.-C., and Chen, C.-Y. (2003). Regional load forecasting in Taiwan-applications of artificial neural networks. Energy conversion and Management, 44, 1941-1949.         [ Links ]

Huang, H., Hwang, R., and Hsieh, J. (2002). A new artificial intelligent peak power load forecaster based on non-fixed neural networks. Electric Power Energy Syst, 24, 245-250.         [ Links ]

Kaukal, M., Akpinar, A., Komurcu, M. I., and Ozsahin, T. S. (2011). Modelling and Forecasting of Turkey's energy consumption using socio-economic and demographic variables. Applied Energy, 88, 1927-1939.         [ Links ]

Lee, C.-C., and Chang, C.-P. (2007). The impact of energy consumption on economic growth: Evidence from linear and nonlinear models in Taiwan. Energy, 32, 2282-2294.         [ Links ]

Lee, C. C., and Chang, C. P. (2005). Structural breaks, energy consumption, and economic growth revisited: evidence from Taiwan. Energy Economics, 27, 857-872.         [ Links ]

Mabel, M. C., and Fernandez, E. (2008). Analysis of wind power generation and prediction using ANN: A case study. Renewable Energy, 33, 986-992.         [ Links ]

Mata, J. (2011). Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Engineering Structures, 33, 903-910.         [ Links ]

Mellit, A., and Kalogirou, S. (2008). Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Comb Sci, 34, 574-632.         [ Links ]

Pao, H. T. (2006). Comparing linear and non-linear forecasts for Taiwan's electricity consumption. Energy (31), 2129-2141.         [ Links ]

Tong, X., Wang, Z., and Yu, H. (2009). A research using hybrid RBF/Elman neural networks for intrusion detection system secure model. Computer Physics Communication, 180, 1795 - 1801.         [ Links ]

Wang, L. (2009). Grey Incidence Analysis between Energy Consumption Structure and Chinese GDP Growth. Proceedings of 2009 IEEE International Conference on Grey Systems and Intelligent Services, November 10-12, 2009, Nanjing, China.         [ Links ]

Wedding, D. K., and Cios, K. J. (1996). Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model. Neurocomputing, 10, 149 - 168.         [ Links ]

World Bank. (1998). World Development Indicators. The International Bank for Reconstruction and Development/the World Bank Washington DC.         [ Links ]

Zuptan, J., and Gasteiger, J., eds. (1999). Neural Networks in Chemistry and Drug Design, Wiley/VCH, New York.         [ Links ]

 

 

Received 15 June 2011
Revised 7 May 2012