A hierarchical artificial neural network for transport energy demand forecast: Iran case study

Title:

A hierarchical artificial neural network for transport energy demand forecast: Iran case study

Creator:

Kazemi, Aliyeh , Shakouri , Hamed G., Menhaj, M. Bagher., Mehregan, Reza, and Neshal, Najmeh

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:85cf3d43-3ca4-437e-8367-c27d7f40fd15
uuid:85cf3d43-3ca4-437e-8367-c27d7f40fd15

Subject:

ANNs, MLP, BP algorithm, forecasting, and transport energy demand

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper presents a neuro-based approach for annual transport energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the transport energy demand, gross domestic product (GDP), population and total number of vehicles are selected. This approach is structured as a hierarchical artificial neural networks (ANNs) model based on the supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This hierarchical ANNs model is designed properly. The input variables are transport energy demand in the last year, GDP, population and total number of vehicles. The output variable is the energy demand of the transportation sector in Million Barrels Oil Equivalent (MBOE). This paper proposes a hierarchical artificial neural network by which the inputs to the ending level are obtained as outputs of the starting levels. Actual data of Iran from 1968-2007 is used to train the hierarchical ANNs and to illustrate capability of the approach in this regard. Comparison of the model predictions with conventional regression model predictions shows its superiority. Furthermore, the transport energy demand of Iran for the period of 2008 to 2020 is estimated.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:6

Harvested from:

CDK

Metadata only:

false