Economics at your fingertips  

Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran

E. Assareh, M.A. Behrang, M.R. Assari and A. Ghanbarzadeh

Energy, 2010, vol. 35, issue 12, 5223-5229

Abstract: This paper presents application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques to estimate oil demand in Iran, based on socio-economic indicators. The models are developed in two forms (exponential and linear) and applied to forecast oil demand in Iran. PSO–DEM and GA–DEM (PSO and GA demand estimation models) are developed to estimate the future oil demand values based on population, GDP (gross domestic product), import and export data. Oil consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the average relative errors on testing data were 2.83% and 1.72% for GA–DEMexponential and GA–DEMlinear, respectively. The corresponding values for PSO were 1.40% and 1.36% for PSO–DEMexponential and PSO–DEMlinear, respectively. Oil demand in Iran is forecasted up to year 2030.

Keywords: GA (genetic algorithm); PSO (particle swarm optimization); Oil; Projection; Demand (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (17) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Series data maintained by Dana Niculescu ().

Page updated 2017-09-29
Handle: RePEc:eee:energy:v:35:y:2010:i:12:p:5223-5229