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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 939305, 14 pages
Research Article

Time Series Analysis and Forecasting for Wind Speeds Using Support Vector Regression Coupled with Artificial Intelligent Algorithms

1School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2MOE Key Laboratory of Western China’s Environmental Systems, Research School of Arid Environment & Climate Change, Lanzhou University, Lanzhou 730000, China
3School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
4China Water Resources Beifang Investigation Design and Research Co. Ltd., Tianjin 300222, China

Received 8 October 2014; Accepted 16 December 2014

Academic Editor: Erol Egrioglu

Copyright © 2015 Ping Jiang 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.


Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC) analysis and a support vector regression (SVR) model that is coupled with brainstorm optimization (BSO) and cuckoo search (CS) algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.