Title :
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
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;
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
DOI :
10.1109/ICCEE.2009.118
