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Mathematical Problems in Engineering
Volume 2014, Article ID 410489, 10 pages
http://dx.doi.org/10.1155/2014/410489
Research Article

Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

1Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China
2School of Science, Hebei University of Engineering, Handan 056038, China
3School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Received 3 December 2013; Accepted 20 January 2014; Published 4 March 2014

Academic Editor: Huaiqin Wu

Copyright © 2014 Jiqiang Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [7 citations]

The following is the list of published articles that have cited the current article.

  • Jiqiang Chen, Witold Pedrycz, Minghu Ha, and Litao Ma, “Set-valued Samples based Support Vector Regression and Its Applications,” Expert Systems with Applications, 2014. View at Publisher · View at Google Scholar
  • Jianzhou Wang, Haiyan Jiang, Bohui Han, and Qingping Zhou, “An Experimental Investigation of FNN Model for Wind Speed Forecasting Using EEMD and CS,” Mathematical Problems in Engineering, vol. 2015, pp. 1–13, 2015. View at Publisher · View at Google Scholar
  • Ping Jiang, Shanshan Qin, Jie Wu, and Beibei Sun, “Time Series Analysis and Forecasting for Wind Speeds Using Support Vector Regression Coupled with Artificial Intelligent Algorithms,” Mathematical Problems in Engineering, vol. 2015, pp. 1–14, 2015. View at Publisher · View at Google Scholar
  • Jie Wan, Jinfu Liu, Guorui Ren, Yufeng Guo, Daren Yu, and Qinghua Hu, “Day-Ahead Prediction of Wind Speed with Deep Feature Learning,” International Journal Of Pattern Recognition And Artificial Intelligence, vol. 30, no. 5, 2016. View at Publisher · View at Google Scholar
  • Sujie Xue, and Xuefeng Yan, “A new kernel function of support vector regression combined with probability distribution and its application in chemometrics and the QSAR modeling,” Chemometrics and Intelligent Laboratory Systems, vol. 167, pp. 96–101, 2017. View at Publisher · View at Google Scholar
  • Qing Zhang, and Xue-Feng Yan, “Improved support vector regression algorithm combining with probability distribution and monotone property,” Kongzhi Lilun Yu Yingyong/Control Theory and Applications, vol. 34, no. 5, pp. 671–676, 2017. View at Publisher · View at Google Scholar
  • Qing Wen, Yapeng Wang, Haodong Zhang, and Zhen Li, “Application of ARIMA and SVM mixed model in agricultural management under the background of intellectual agriculture,” Cluster Computing, 2018. View at Publisher · View at Google Scholar