A Short-term Wind Speed Forecasting Model Based on Improved QPSO Optimizing LSSVM

被引：0
|
作者：
Hu, Zhiyuan [1 ]
Liu, Qunying [2 ]
Tian, Yunxiang [1 ]
Liao, Yongfeng [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China
[2] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China
来源：
2014 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON) | 2014年
关键词：
quantum particle swarm optimization; least squares support vector machine; weight factor; parameters optimization; windspeed forecasting;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Theaccuracy of short-term wind forecasting is important to guaranteethe accuracy of wind farm power forecasting. Animproved QPSO(Quantum Particle Swarm Optimization) algorithm for LSSVM(Least Squares Support Vector Machine) parameters selection isproposed based on the analysis of the QPSO and LSSVM. And then, with the weight factor m(best) (average optimal position of the particle swarm) beingintroduced, the global search capability of QPSO is improved to optimize important parametersduring the modeling process, by which the generalization capability and learning performance of LSSVM model is improved. The simulation results show that the proposed method can significantly improve the predicting accuracy. However, the mean error of the predicted wind velocity is only 2.43%, which satisfies the requirements of predicting accuracy.
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页数：6
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