Authors
Abstract
In this paper, the energy demand of transport sector from 1386 to 1400 was
forecasted using artificial neural networks (ANN) approach considering
economic and social indicators. Feed forward supervised neural networks to
forecast and back propagation algorithm to train networks were used. In
order to analyze the influence of economic and social indicators on energy
demand of transport sector, Gross Domestic Product (GDP), population and
the total number of vehicles in 1347-1385 were taken into consideration. The
obtained results as compared with the multiple regression method, revealed
much less mistakes. The average absolute error percentage was decreased
from 15.52% to 6.05%.
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Keywords
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