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

被引:183
作者
Assareh, E. [1 ]
Behrang, M. A. [1 ]
Assari, M. R. [2 ]
Ghanbarzadeh, A. [3 ]
机构
[1] Islamic Azad Univ, Dezful Branch, Dept Mech Engn, Tehran, Iran
[2] Jundi Shapour Univ, Fac Engn, Dept Mech Engn, Dezful, Iran
[3] Shahid Chamran Univ, Fac Engn, Dept Mech Engn, Ahvaz, Iran
关键词
GA (genetic algorithm); PSO (particle swarm optimization); Oil; Projection; Demand; ELECTRICITY DEMAND; ENERGY-CONSUMPTION;
D O I
10.1016/j.energy.2010.07.043
中图分类号
O414.1 [热力学];
学科分类号
摘要
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. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:5223 / 5229
页数:7
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