Journal
APPLIED ENERGY
Volume 142, Issue -, Pages 266-273Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2014.12.045
Keywords
Genetic Algorithm; Neural Network; West Texas Intermediate crude oil price; Backpropagation algorithms
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Funding
- University of Malaya High Impact Research Grant from Ministry of Higher Education Malaysia [UM.C/625/HIR/MOHE/SC/13/2]
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This paper proposes an alternative approach based on a genetic algorithm and neural network (GA-NN) for the prediction of the West Texas Intermediate (WTI) crude oil price. Comparative simulation results suggested that the proposed GA-NN approach is better than the baseline algorithms in terms of prediction accuracy and computational efficiency. Mann-Whitney test results indicated that the WTI crude oil price predicted by the proposed GA-NN and the observed price are statistically equal. Further comparison of the proposed GA-NN with previous studies indicated performance improvement over existing results. The proposed model can be useful in the formulation of policies related to international crude oil price estimations, development plans and industrial production. (C) 2014 Elsevier Ltd. All rights reserved.
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