Predicting gasoline consumption in Iran using deep learning and time series approaches

Today, energy shortages are a serious issue to achieve economic development, which is why demand management is an attractive concern for countries. In Iran, the transportation sector has a major share of energy consumption, 99.7% of which belongs to gasoline. The increasing trend of gasoline consumption in Iran, insufficient domestic production, significant growth of gasoline imports in recent years show the incremental importance of managing gasoline consumption in Iran, so, predicting the consumption process as accurately as possible can be very useful in achieving this. This study predicts gasoline consumption using monthly data by comparing the efficiency of three methods, networks with long-term and short-term memory, recursive self-organizing maps, and the traditional method of moving the average seasonal auto-regression. The results indicate that the use of 12-month time-frequency for data training had more accurate results compared to other data frequencies, and the deep learning method of networks with long-term short-term memory was more efficient than the other two methods.