Transport energy demand forecast using multi-level genetic programming-Reference-Cited by-同舟云学术

Transport energy demand forecast using multi-level genetic programming

Published:2012-03 Issue:1 Volume:91 Page:496-503
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Forouzanfar Mehdi,Doustmohammadi A.,Hasanzadeh Samira,Shakouri G H.

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference30 articles.

1. Iran Ministry of Energy, Deputy of Electricity and Energy Affairs, Energy Planning Department, Energy Balances of Islamic Republic of Iran; 2005.

2. Kazemi A, Shakouri GH, Menhaj MB, Taghizadeh MR. Multi-level artificial neural networks for modeling transport energy demand. IIES international 13th oil & gas conference, energy management; policy and experiences, Tehran, I.R. Iran; November 30–December 1, 2008.

3. Taghizadeh MR, Shakouri GH, Menhaj MB, Kazemi A, Design of a multi-level fuzzy linear regression model for forecasting transport energy demand. IIES international 13th oil & gas conference, energy management; policy and experiences, Tehran, I.R. Iran; November 30–December 1, 2008.

4. Genetic programming for economic modeling;Koza,1995

5. Genetic programming prediction of stock prices;Kaboudan;Comput Econ,2000

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