Abstract
In this study, we select Middle East countries involving Jordan, Lebanon, Oman, and Saudi Arabia for modeling oil consumption based on computational intelligence methods. The limitations associated with Levenberg-Marquardt (LM) Neural Network (NN) motivated this research to optimize the parameters of NN through Artificial Bee Colony Algorithm (ABC-LM) to build a model for the prediction of oil consumption. The proposed model was competent to predict oil consumption with improved accuracy and convergence speed. The ABC-LM performs better than the standard LMNN, Genetically optimized NN, and Back-propagation NN. The proposed model may guide policy makers in the formulation of domestic and international policies related to oil consumption and economic development. The approach presented in the study can easily be implemented into a software for use by the government of Jordan, Lebanon, Oman, and Saudi Arabia.
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References
Energy Information Administration of the US Department of Energy (2013), http://www.eia.gov/
Assareh, E., Behrang, M.A., Assari, M.R., Ghanbarzadeh, A.: Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35(12), 5223–5229 (2010)
Toksarı, M.D.: Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy 35(8), 3984–3990 (2007)
Chiroma, H., Abdulkareem, S., Abubakar, A., Zeki, A., Gital, A.Y., Usman, M.J.: Co – Active Neuro-Fuzzy Inference Systems Model for Predicting Crude Oil Price based on OECD Inventories. In: 3rd International Conference on Research and Innovation in Information Systems (ICRIIS 2013), Malaysia, pp. 232–235. IEEE (2013)
Chiroma, H., Abdulkareem, S., Sari, E.N., Abdullah, Z., Muaz, S.A., Kaynar, O., Shah, H., Herawan, T.: Soft Computing Approach in Modeling Energy Consumption. In: Murgante, B., et al. (eds.) ICCSA 2014, Part VI. LNCS, vol. 8584, pp. 770–782. Springer, Heidelberg (2014)
Kaynar, O., Yilmaz, I., Demirkoparan, F.: Forecasting of natural gas consumption with neural network and neuro fuzzy system. In: EGU General Assembly Conference Abstracts, vol. 12, p. 7781 (2010)
Yusof, N.M., Rashid, R.S.A., Mohamed, Z.: Malaysia crude oil production estimation: An application of ARIMA model. In: 2010 International Conference on Science and Social Research (CSSR), pp. 1255–1259 (2010)
Su, F., Wu, W.: Design and testing of a genetic algorithm neural network in the assessment of gait patterns. Medical Engineering Physics 22, 67–74 (2000)
Dehuri, S., Cho, S.B.: A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Computing and Applications 19(2), 317–328 (2000)
Bishop, M.C.: Pattern recognition and machine learning. Springer, New York (2006)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 5(1), 687–697 (2008)
Azar, A.T.: Fast neural network learning algorithms for medical applications. Neural Computing and Applications 23(3-4), 1019–1034 (2013)
He, K., Chi, X., Chen, S., Lai, K.K.: Estimating VaR in crude oil market: A novel multi–scale non-linear ensemble approach incorporating wavelet analysis and neural network. Neurocomputing 72, 3428–3438 (2009)
Ekonomou, L.: Greek long-term energy consumption prediction using artificial neural networks. Energy 35(2), 512–517 (2010)
Khazem, H.A.: Using artificial neural network to forecast the futures prices of crude oil. PhD dissertation, Nova south Eastern University, Florida (2007)
Jammazi, R., Aloui, C.: Crude oil forecasting: experimental evidence from wavelet decomposition and neural network modelling. Energy Economics 34, 828–841 (2012)
Pan, T.Y., Wang, R.Y.: State space neural networks for short term rainfall–runoff forecasting. Journal of Hydrology 297, 34–50 (2004)
Peter, G.Z., Patuwo, B.E., Hu, M.Y.: A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers & Operation Research 28, 381–396 (2001)
Nawi, N.M., Rehman, M.Z., Khan, A.: Countering the problem of oscillation in Bat-BP gradient trajectory by using momentum. In: Herawan, T., Deris, M.M., Abawajy, J. (eds.) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). LNEE, vol. 285, pp. 103–118. Springer, Heidelberg (2014)
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Chiroma, H. et al. (2015). An Intelligent Modeling of Oil Consumption. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_50
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DOI: https://doi.org/10.1007/978-3-319-11218-3_50
Publisher Name: Springer, Cham
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