Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine

  • Sun G
  • Chen Y
  • Wei Z
  • et al.
Citations of this article
Mendeley users who have this article in their library.


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.




Sun, G., Chen, Y., Wei, Z., Li, X., & Cheung, K. W. (2014). Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine. Journal of Applied Mathematics, 2014, 1–6.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free