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Journal of Applied Mathematics
Volume 2014, Article ID 437592, 6 pages
Research Article

Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine

1Research Center for Renewable Energy Generation Engineering, Ministry of Education, Hohai University, Nanjing 210098, China
2ALSTOM GRID Technology Center Co., Ltd., Shanghai 201114, China
3ALSTOM Grid Inc., Redmond, WA 98052, USA

Received 31 December 2013; Revised 5 May 2014; Accepted 22 May 2014; Published 12 June 2014

Academic Editor: Hongjie Jia

Copyright © 2014 Guoqiang Sun 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.


With the development of wind power technology, the security of the power system, power quality, and stable operation will meet new challenges. So, in this paper, we propose a recently developed machine learning technique, relevance vector machine (RVM), for day-ahead wind speed forecasting. We combine Gaussian kernel function and polynomial kernel function to get mixed kernel for RVM. Then, RVM is compared with back propagation neural network (BP) and support vector machine (SVM) for wind speed forecasting in four seasons in precision and velocity; the forecast results demonstrate that the proposed method is reasonable and effective.