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Abstract and Applied Analysis
Volume 2014, Article ID 984268, 21 pages
Research Article

Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

1Gansu Meteorological Service Center, Lanzhou, Gansu 730020, China
2School of Mathematics & Statistics, Lanzhou University, Lanzhou, Gansu 730000, China
3Gansu Meteorological Information & Technique Support & Equipment Center, Lanzhou, Gansu 730020, China

Received 18 February 2014; Accepted 18 April 2014; Published 9 June 2014

Academic Editor: Fuding Xie

Copyright © 2014 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.


As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.