A Hybrid Artificial-Intelligence Predictive Model for Crude Oil Demand : A Case Study for a High Producer and a High Consumer

This paper develops a rigorous and advanced computational model to describe, analyze, and forecast global crude oil demand. The paper deploys a hybrid approach of artificial intelligence techniques: artificial neural network and genetic algorithms, to devise a methodological framework for developing forecasting models of global crude oil demand. We piloted two country cases of a high oil producer (Saudi Arabia) and a high oil consumer (China) to illustrate the effectiveness and applicability of the proposed methodology for developing oil demand outlook using artificial intelligence.The input variables of the neural network models include gross domestic product, population, oil prices, gas prices, and transport data, in addition to transformed variables and functional links. The artificial intelligent predictive models of oil demand were successfully developed, trained, validated and tested using historical oil-market data yielding excellent predictions of oil demand. The performance of the intelligent models of Saudi Arabia and China were examined for generalization attribute, predictability, and accuracy. Oil demand models for Saudi Arabia and China achieved a high prediction accuracy of a correlation coefficient of 0.975 and 0.996, respectively.The intelligent outlook models show that crude oil demand for both Saudi Arabia and China will continue to increase for the outlook period (2018-2022) but with mildly declining growth. This falling growth of oil demand can be attributed to the increase in energy efficiency, fuel switching, conversion of power plants from crude to gas-based plants, and an increase in the utilization of renewable energy such as solar and wind for electric generation and water desalination.The methodology proposed improves and enhances the conventional process of developing the oil demand outlook. It also improves and enhances the predictability and accuracy of current forecasting models of oil demand. In this study, features selection techniques are applied to identify and understand the endogenous and exogenous factors that influence global energy markets, particularly those factors that impact and drive global oil demand