A New Planning to Forecasting Fuel Consumption in Iran Transportation Using a Hybrid Algorithm and Artificial Neural Network

Maryam Dehghanbaghi

Maryam Dehghanbaghi Department of Industrial Engineering, Faculty of Engineering, Robat Karim Branch, Islamic Azad University, Tehran, Iran.

Keywords: Artificial Neural Network, Hybrid Algorithm, Imperialist Competitive Algorithm, Forecasting, Simulated Annealing.

Abstract

Forecasting fuel demand is one of the preconditions for energy planning and management. Fossil fuels, a major part of which is consumed in transportation, are one of the most important energies. Therefore, forecasting fuel demand in transportation is of particular importance. An MLP perceptron neural network has been proposed in this study, which is trained using a Hybrid algorithm. The hybrid algorithm is based on an imperialist competitive algorithm (ICA), in which the specifications of simulated annealing (SA) algorithm have been used. To perform the forecasting, the effective parameters on road transportation including the amounts of ton kilometer of transported goods, person kilometer of passengers, the average age of cargo fleet, and the average age of passenger fleet were identified. The results of the investigation indicate the effective performance of the proposed algorithm in ANN training.

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