A fuzzy regression approach for improvement of gasoline consumption estimation with uncertain data

Gas consumption plays a vital role in socio-economic development of most countries. This study presents a fuzzy regression algorithm for forecasting gas consumption based on standard economic indicators. The proposed approach utilises the most standard independent variables for the regression models. The standard input indicators are gross domestic production, population, number of vehicles and actual price of gasoline. To show the applicability and superiority of the proposed fuzzy regression model, the data for gasoline consumption in Iran from 1992 to 2005 is used. The results show that the fuzzy regression approach provides better solution than conventional regression for gas consumption estimation in Iran. The algorithm may be used by policy makers to accurately foresee the behaviour of gas consumption in various regions.