A Hierarchical Artificial Neural Network for Gasoline Demand Forecast of Iran

Abstract This paper presents a neuro-based approach for annual gasoline demand forecast in Iran by taking into account several socio-economic indicators. To analyze the influence of economic and social indicators on the gasoline demand, gross domestic product (GDP), population and the total number of vehicles are selected. This approach is structured as a hierarchical artificial neural network (ANN) based on supervised multi-layer perceptron (MLP), trained with back-propagation (BP) algorithm. This hierarchical ANN is designed properly. The input variables are GDP, population, total number of vehicles and the gasoline demand in the last one year. The output variable is the gasoline demand. The paper proposes a hierarchical network by which the inputs to the ending level are obtained as outputs of the starting levels. Actual Iranian data between ۱۹۶۷ and ۲۰۰۸ were used to test the hierarchical ANN hence; it illustrated the capability of the approach. Comparison of the model predictions with validation data shows validity of the model. Furthermore, the demand for the period between ۲۰۱۱ and ۲۰۳۰ is estimated. It is noticeable that if there will not be any price shock or efficiency improvement in the transportation sector, the gasoline consumption may achieve a threatening level of about ۵۴ billion liters by ۲۰۳۰ in Iran.

کلیدواژه ها:

ANN ، MLP ، BP Algorithm ، Forecasting ، Gasoline Demand ، واژگان کلیدی: شبکه های عصبی مصنوعی ، پرسپترون چندلای ، الگوریتم پس انتشار خطا ، پیش بینی ، تقاضای بنزین