Abstract

Energy industry in Malaysia is one of critical sector that plays a major role in contributing the nation economic and industrial growth. A forecasting model is required to be developed to provide the oil demand forecast in transportation sector. This research analyses different forecasting models including Artificial Neural Network (ANN) model to predict the future oil demand in transportation sector in Malaysia. In order to select the best forecasting model, the model validation is done using the error analysis techniques. Based on the model validation result, it is found that the Artificial Neural Network (Multivariate) model gives the least error in all the error analysis techniques. The model forecast that the oil demand in transportation sector in Malaysia for the year 2020, 2025 and 2030 would be 559.44, 581.779 and 609.941 kg of oil equivalent respectively.

Keywords: Oil consumption; demand forecasting; transportation sector; forecasting models; Aritificial Neural Network (ANN); model validation

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How to Cite
[1]
Al-Qahtani, A.M. and S., J. 2019. Oil Demand Forecasting in Malaysia in Transportation Sector Using Artificial Neural Network. International Journal of Science and Engineering Invention. 5, 02 (Jan. 2019), 08 to 15. DOI:https://doi.org/10.23958/ijsei/vol05-i01/132.

Copyrights & License

Copyright © 2019 Ali Mubarak Al-Qahtani Jebaraj S. this is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.