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Short-term prediction of wind power based on IPSO-LSSVM
Author(s): 
Pages: 107-112
Year: Issue:  24
Journal: Power System Protection and Control

Keyword:  风电功率预测改进粒子群算法最小二乘支持向量机IPSO-LSSVM误差分析;
Abstract: 风电功率预测的关键是预测模型的选择和模型性能的优化.选择最小二乘支持向量机(least squares support vector machine,LSSVM)作为风电功率预测模型,使用改进的粒子群算法(improved particle swarm optimization algorithm,IPSO)对影响最小二乘支持向量机回归性能的参数进行优化.在建立了改进的粒子群算法优化最小二乘支持向量机(LSSVM)的风电功率预测模型后,运用该模型对广西某风电场进行了仿真研究.为了对比研究,同时使用前馈(back propagation,BP)神经网络模型和支持向量机(support vector machine,SVM)模型进行了预测.最后采用多种误差指标对三种模型的预测结果进行综合分析.结果表明,使用改进的粒子群算法优化最小二乘向量机(IPSO-LSSVM)的风电功率预测模型具有较高的预测精度.
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