At present, liquid fuels remain the dominant source of transportation energy consumption all over the world. Accordingly, the future demand prediction of petroleum consumption is a very challenging task with regard to efficient supply management. In this paper, a hybrid SVR-DE model is developed and proposed to address the problem. The developed model takes ability of SVR model to formulate complex predictive function while DE algorithm is used to search the optimal parameters of SVR model. Moreover, the hybrid model is compared withboth ARIMA and SVR models as traditional single models. The experimental results indicated that the developed model outperforms traditional forecasting models based on MAE, MAPE, and sMAPE criteria. Furthermore, the forecast performance of hybrid model is significantly different from both traditional single models at 0.05 significance level. Consequently, the proposed model can be a promising tool for annual petroleum consumption.
