Determination of Social-Economic Indicators for Gasoline and Petroleum Consume Prediction Using Artificial Neural Network (ANN) In Iran

Due to various changes in Gasoline and petroleum consumption, it is difficult to model it with conventional methods. This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The objective of this study is to propose prediction models of Gasoline and petroleum, using ANN based on social-economic indicators In Iran. The study develops a feed-forward neural network models, using social-economic indicators to make more accurate predict of the energy consumption and correct investments in Iran. In this study, two different models for each energy carrier demand were used in order to train the ANN. In order to train the neural network, economic and energy data for last 44 years between years 1347 and 1390 (1969-2012) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of energy carrier demand. The proposed approach can be useful in the effective implementation of energy policies, since accurate predictions of energy consumption affect the capital investment, the environmental quality, the revenue analysis, the market research management, while conserve at the same time the supply security.