A new artificial intelligence GANNATS model predicts gasoline demand of Saudi Arabia

Abstract

This paper develops a novel AI and data-driven predictive model to analyze and forecast energy markets, and tests it for gasoline demand of Saudi Arabia. The AI model is based on a genetic algorithm (GA), artificial neural network (ANN), and data mining (DM) approach for time-series (TS) analysis, referred to as GANNATS. The GANNATS predictive model was successfully designed, trained, validated, and tested using real historical market data. Results show that the model yields accurate predictions with robust key performance indicators. A double cross-validation of the model verified that Saudi Arabia's gasoline demand declined by 2.5% in 2017 from its 2016 level. The model forecasts that Saudi gasoline demand will maintain a mild growth over the short-term outlook. Variables impact and screening analysis was performed to identify the influencing factors driving the gasoline demand. The recent decline in Saudi gasoline demand is primarily attributed to the improvements in vehicle efficiency, lifting of fuel price subsidies, declining population growth, and changes in consumer behavior. This paper enriches existing knowledge of best practices for forecasting domestic and global gasoline demand. In addition, the methodology presented improves on traditional econometric models and enhances the predictability and accuracy of forecasts of gasoline demand.