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Particle Swarm Optimization Approach for Estimation of Energy Demand of Turkey

机译:估计土耳其能源需求的粒子群优化方法

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This paper presents an application of Particle Swarm Optimization (PSO) technique to estimate energy demandof Turkey, based on economic indicators.The ec onomic indicators that are used during the model development are: gross national product (GNP), population, import and export figures of Turkey. Energy demand and other economic indicators in Turkey from 1979 to 2005 are considered as the case of this study. The energy estimation model based on PSO (EEPSO) is developed in two forms (linear (EEPSOL) and quadratic (EEPS OQ))and applied to forecast energy demand in Turkey. PSOQ form provided better-fit solution due to fluctuations of the economic indicators. In order to show the accuracy of the algorithm, some comparisons are made with previous studies which are using Ant Colony Optimization (ACO) and PSO. The future energy demand is calculated under different scenarios. The relative estimation errors of the proposed models are the lowe st when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.
机译:本文介绍了基于经济指标的粒子群优化(PSO)技术估算土耳其的能源需求。模型开发过程中使用的经济指标包括:国民生产总值(GNP),人口,进出口数据土耳其。 1979年至2005年土耳其的能源需求和其他经济指标被视为本研究的案例。基于PSO(EEPSO)的能源估算模型以线性(EEPSOL)和二次方(EEPS OQ)两种形式开发,并用于预测土耳其的能源需求。由于经济指标的波动,PSOQ表格提供了更好的解决方案。为了显示该算法的准确性,与以前的研究(使用蚁群优化(ACO)和PSO)进行了一些比较。未来能源需求是在不同情况下计算得出的。与能源和自然资源部(MENR)的预测相比,所提出模型的相对估计误差较低。

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