上海交通大学学报

上海交通大学学报（自然版） ›› 2011, Vol. 45 ›› Issue (08): 1125-1129.

• 自动化技术、计算机技术 • 上一篇    下一篇

基于改进最小二乘支持向量机方法的短期风速预测

张广明，袁宇浩，龚松建   

  1. (南京工业大学 自动化与电气工程学院，南京 210009)
  • 收稿日期:2011-04-25 出版日期:2011-08-30 发布日期:2011-08-30
  • 基金资助:

    江苏省科技厅工业科技支撑计划项目（BE2009166）

A Predictive Model of Short-Term Wind Speed Based on Improved Least Squares Support Vector Machine Algorithm

 ZHANG  Guang-Ming, YUAN  Yu-Hao, GONG  Song-Jian   

  1. (School of Automation & Electrical Engineering, Nanjing University of Technology, Nanjing 210009, China)
  • Received:2011-04-25 Online:2011-08-30 Published:2011-08-30

摘要： 为了进一步提高短期风速预测的精度，分析了一种改进的风速预测方法.该方法考虑风速发生变化的极值点对总体预测误差的影响，以及预测曲线较实际曲线产生的滞后，分别对预测数据进行了极值点修正和偏移量处理.在对未来1 h风速进行预测时，相比粒子群优化(PSO)的最小二乘支持向量机(LS-SVM)模型、未经优化的LS-SVM模型及反向传播(BP)神经网络模型，所提出的模型具有较高的预测精度和运算速度.算例结果表明，改进的LS-SVM算法是进行短期风速预测的有效方法.

关键词: 风速预测, 粒子群优化, 最小二乘支持向量机, 极值点, 偏移量

Abstract: In order to improve the forecast precision, an improved wind speed forecasting algorithm was discussed. The new method has modified extreme points and processed offset of predicting data, considering with the extreme points of the change in wind speed affecting the prediction error and the delay of prediction curve compared with actual wind speed. The forecasting model has better prediction accuracy and better computing speed to predict wind speed for the next one hour, compared with the wind speed model based on least squares support vector machine optimized by particle swarm optimization algorithm(PSO-LS-SVM), least squares support vector machine (LS-SVM) and back propagation (BP) neural network. The simulation results show that the improved least squares support vector machine is an effective method for short-term wind forecasting.

Key words:  wind speed forecasting, particle swarm optimization (PSO), least squares support vector machine (LSSVM), extreme points, offset

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