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

被引：187

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

来源：

ENERGY | 2010年 / 35卷 / 12期

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

页码：5223 / 5229

页数：7

共 42 条

[1] Grey prediction with rolling mechanism for electricity demand forecasting of Turkey [J].

Akay, Diyar ;

Atak, Mehmet .

ENERGY, 2007, 32 (09) :1670-1675

[2] Electricity demand for Sri lanka: A time series analysis [J].

Amarawickrama, Himanshu A. ;

Hunt, Lester C. .

ENERGY, 2008, 33 (05) :724-739

[3] Estimation of electricity demand of Iran using two heuristic algorithms [J].

Amjadi, M. H. ;

Nezamabadi-pour, H. ;

Farsangi, M. M. .

ENERGY CONVERSION AND MANAGEMENT, 2010, 51 (03) :493-497

[4] Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm [J].

Amjady, N. ;

Keynia, F. .

ENERGY, 2009, 34 (01) :46-57

[5]

[Anonymous], 2005, EN BAL ANN REP

[6]

[Anonymous], 1999, Genetic Algorithms + Data Structures = Evolution Programs

[7]

[Anonymous], 1991, Foundations of Genetic Algorithms

[8] An oil demand and supply model incorporating monetary policy [J].

Askari, Hossein ;

Krichene, Noureddine .

ENERGY, 2010, 35 (05) :2013-2021

[9] A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran [J].

Azadeh, A. ;

Ghaderi, S. F. ;

Sohrabkhani, S. .

ENERGY POLICY, 2008, 36 (07) :2637-2644

[10] An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran [J].

Azadeh, A. ;

Saberi, M. ;

Seraj, O. .

ENERGY, 2010, 35 (06) :2351-2366

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