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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 395815, 7 pages
Research Article

Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China

Received 30 October 2012; Revised 29 December 2012; Accepted 30 December 2012

Academic Editor: Sheng-yong Chen

Copyright © 2013 Chuanan Yao 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.


Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN) and loose wavelet neural network (LWNN) in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.