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Journal of Applied Mathematics
Volume 2014, Article ID 835791, 11 pages
http://dx.doi.org/10.1155/2014/835791
Research Article

Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting

1School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
2Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China
3VLSI Lab, Nanyang Technological University, Singapore 639798
4School of Information Science and Engineering, Hunan University, Changsha 410082, China

Received 16 November 2013; Revised 18 April 2014; Accepted 23 April 2014; Published 17 June 2014

Academic Editor: Hongjie Jia

Copyright © 2014 Hong Zhang 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 [3 citations]

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

  • Hua Xie, Min Ding, Luefeng Chen, Jianqi An, Zhangbing Chen, and Min Wu, “Short-term wind power prediction by using empirical mode decomposition based GA-SYR,” 2017 36th Chinese Control Conference (CCC), pp. 9175–9180, . View at Publisher · View at Google Scholar
  • El-Mahjoub Boufounas, and Aumeur El Amrani, “Optimal multivariable control for wind energy conversion systems using particle swarm optimization technique,” Control and Intelligent Systems, vol. 45, no. 4, pp. 199–207, 2017. View at Publisher · View at Google Scholar
  • Qi Liu, Vinay Prasad, Khushaal Popli, Victor Maries, and Artin Afacan, “Development of a vision-based online soft sensor for oil sands flotation using support vector regression and its application in the dynamic monitoring of bitumen extraction,” Canadian Journal of Chemical Engineering, 2018. View at Publisher · View at Google Scholar