基于混合智能优化LSSVM的非高斯脉动风速预测

PDF(2185 KB)

振动与冲击 ›› 2017, Vol. 36 ›› Issue (20) : 52-58.

论文

李春祥1;丁晓达1;郑晓芬2

作者信息 +

Predicting non-Gaussian wind velocity using hybridizing intelligent optimization based LSSVM

Li Chunxiang1;Ding Xiaoda 1; Zheng Xiaofen2

Author information +

文章历史 +

摘要

考虑智能优化：蚁群算法（ACO）、遗传算法（GA）和粒子群算法（PSO）各自优缺点，并为充分发挥蚁群、遗传算法较好的全局搜索能力和粒子群算法的分级搜索机制，提出混合蚁群和粒子群优化（ACO+PSO）和混合遗传算法和粒子群优化（GA+PSO）最小二乘支持向量机（LSSVM）的非高斯脉动风速预测模型，分别称为ACO+PSO-LSSVM和GA+PSO-LSSVM。运用ACO+PSO-LSSVM和GA+PSO-LSSVM预测模型对某超高层建筑的非高斯脉动风速进行了预测；为比较目的，同时给出ACO-LSSVM、PSO-LSSVM和GA-LSSVM的非高斯脉动风速预测结果。经仔细检查非高斯脉动风速时程预测值、相关函数预测值以及预测性能评价指标，验证了基于混合智能优化LSSVM对非高斯脉动风速预测的有效性和优势。

Abstract

Taking into account the respective strengths and weaknesses of the ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and, in order to allow full play to the fine global search capabilities of both ACO and GA and the hierarchical search mechanism of PSO, both the hybridizing ant colony and particle swarm optimization（named ACO+PSO）and hybridizing genetic algorithm and particle swarm optimization（called GA+PSO）based least square support vector machines (LSSVM), referred respectively to as ACO+PSO-LSSVM and GA+PSO-LSSVM, have been proposed to predict the non-Gaussian fluctuating wind velocity. Subsequently, the non-Gaussian wind velocity of a super-tall building is forecasted by use of ACO+PSO-LSSVM and GA+PSO-LSSVM. For the purpose of comparison, the results for the non-Gaussian wind velocity of ACO-LSSVM, PSO-LSSVM, and GA-LSSVM are provided simultaneously. Through scrutinizing the predicted non-Gaussian wind velocity time-history, predicted values of correlation functions, and predictive performance evaluation indices, it has been shown that the two proposed
hybridizing intelligent optimization based LSSVM algorithms possess better accuracy and higher robustness for the prediction of non-Gaussian fluctuating wind velocity of super-tall buildings.

导出引用

李春祥1;丁晓达1;郑晓芬2. 基于混合智能优化LSSVM的非高斯脉动风速预测[J]. 振动与冲击, 2017, 36(20): 52-58

Li Chunxiang1;Ding Xiaoda 1; Zheng Xiaofen2. Predicting non-Gaussian wind velocity using hybridizing intelligent optimization based LSSVM[J]. Journal of Vibration and Shock, 2017, 36(20): 52-58

参考文献

[1] 迟恩楠，李春祥. 基于优化组合核和Morlet小波核的LSSVM脉动风速预测方法 [J]. 振动与冲击, 2016，35（18）:52-57.
Li Chunxiang, Chi Ennan. Forecasting fluctuating wind velocity using optimized combination kernel and Morlet wavelet kernel based LSSVM [J]. Journal of Vibration and Shock, 2016，35（18）: 52-57.
[2] 李春祥, 迟恩楠, 何亮, 李正农. 基于时变ARMA和EMD-PSO-LSSVM算法的非平稳下击暴流风速预测 [J]. 振动与冲击, 2016，35（17）: 33-38, 51.
Li Chunxiang, Chi En-nan, He Liang, Li Zhengnong. Prediction of nonstationary downburst wind velocity based on time-varying ARMA and EMD- PSO-LSSVM algorithms [J]. Journal of Vibration and Shock, 2016，35（17）: 33-38, 51.
[3] 迟恩楠，李春祥. 基于小波和乘法混合核函数LSSVM的顺风向非高斯空间风压预测 [J]. 振动与冲击, 2017,36（16）.
Chi En-nan, Li Chunxiang. Forecasting non-Gaussian spatial along-wind pressure using wavelet kernel and multiplicative mixed kernel functions based LSSVM [J]. Journal of Vibration and Shock, 2017,36（16）.
[4] Chappelle O, Vapnik V, Bousquet O. Choosing multiple parameters for support vector machines [J]. Machine Learning, 2002, 46(1): 131−160.
[5] 杨洪，古世甫，崔明东，孙禹. 基于遗传优化的最小二乘支持向量机风电场风速短期预测 [J]. 电力系统保护与控制, 2011, 39(11): 44−48.
Yang Hong, Gu Shi-fu, Cui Ming-dong, Sun Yu. Forecast of short-term wind speed in wind farms based on GA optimized LS-SVM. Power System Protection and Control. , 2011, 39(11): 44−48.
[6] 曾杰, 张华. 基于蚁群优化的最小二乘支持向量机风速预测模型研究 [J]. 太阳能学报, 2011, 32(3): 296−300.
Zeng Jie, Zhang Hua. Wind speed forecasting model study based on Least squares support vector machine and ant colony optimization. Acta Energiae Solaris Sinica, 2011, 32(3): 296−300.
[7] Chamkalani Ali, Zendehboudi Sohrab, Bahadori Alireza, Kharrat Riaz, Chamkalani Reza, James Lesley, Chatzis Ioannis. Integration of LSSVM technique with PSO to determine asphaltene deposition [J]. Journal of Petroleum Science and Engineering, 2014, 124: 243-253.
[8] 李春祥,丁晓达, 叶继红. 基于混合蚁群和粒子群优化LSSVM的脉动风速预测 [J]. 振动与冲击, 2016，35（21）:131-136.
Li Chunxiang, Ding xiaoda, Ye Jihong. Fluctuating wind velocity forecasting of hybridizing ant colony and particle swarm optimization based LSSVM [J]. Journal of Vibration and Shock, 2016，35（21）:131-136.
[9] Suykens JA,Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293–300.
[10] Suykens JAK, De Brabanter J, Lukas L, Vandewalle J. Weighted least squares support vector machines: Robustness and sparse approximation. Neurocomputing, 2002, 48(1–4): 85–105.