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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 608597, 18 pages
Research Article

Short-Term Wind Speed Forecasting Study and Its Application Using a Hybrid Model Optimized by Cuckoo Search

1Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Arid Climatic Change and Disaster Reduction of Chinese Meteorological Administration, Institute of Arid Meteorology, Chinese Meteorological Administration, Lanzhou 730020, China
2Gansu Meteorological Service Center, Lanzhou, Gansu 730020, China
3School of Mathematics & Statistics, Lanzhou University, Lanzhou, Gansu 730000, China
4MOE Key Laboratory of Western China’s Environmental Systems, Research School of Arid Environment & Climate Change, Lanzhou University, Lanzhou 730000, China
5Scientific Information Center for Resources and Environment, Lanzhou Branch of the National Science Library, Chinese Academy of Sciences, Lanzhou 730000, China
6Datong Meteorological Bureau of Shanxi Province, Datong 037010, China

Received 15 October 2014; Accepted 16 January 2015

Academic Editor: Erol Egrioglu

Copyright © 2015 Xuejun 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.


The support vector regression (SVR) and neural network (NN) are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H) and wavelet denoising (WD) techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS) outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.