A Multi-level Artificial Neural Network for Gasoline Demand Forecasting of Iran

Author

Kazemi, A. ; Shakouri, H.G. ; Mehregan, M.R. ; Taghizadeh, M.R. ; Menhaj, M.B. ; Foroughi, A.A.

Author_Institution

Univ. of Tehran, Tehran, Iran

Volume

1

fYear

2009

fDate

28-30 Dec. 2009

Firstpage

61

Lastpage

64

Abstract

This paper presents a neuro-based approach for Iran annual gasoline demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the gasoline demand, the gross domestic product (GDP), the population and the total number of vehicles are selected. This approach is structured as a multi-level artificial neural network (ANN) based on supervised multi-layer perceptron (MLP), trained with the backpropagation (BP) algorithm. This multi-level ANN is designed properly. Actual data of Iran from 1968-2006 is used to train the multi-level ANN and illustrate capability of the approach in this regard. Comparison of the model predictions with data of the evaluating period shows validity of the model. Furthermore, the demand for the period of 2007 to 2030 is estimated.

Keywords

backpropagation; economic forecasting; economic indicators; learning (artificial intelligence); multilayer perceptrons; petroleum; backpropagation algorithm; gasoline demand forecasting; gross domestic product; multilevel artificial neural network; supervised multilayer perceptron; Artificial neural networks; Demand forecasting; Economic forecasting; Economic indicators; Load forecasting; Neurons; Petroleum; Power generation economics; Predictive models; Transportation; ANN; BP algorithm; Forecasting; Gasoline demand; MLP;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Electrical Engineering, 2009. ICCEE '09. Second International Conference on

Conference_Location

Dubai

Print_ISBN

978-1-4244-5365-8

Electronic_ISBN

978-0-7695-3925-6

Type

conf

DOI

10.1109/ICCEE.2009.118

Filename

5380667