Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine
Joint Authors
Li, Xiaolu
Chen, Yue
Wei, Zhinong
Cheung, Kwok W.
Sun, Guoqiang
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-12
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sun, Guoqiang& Chen, Yue& Wei, Zhinong& Li, Xiaolu& Cheung, Kwok W.. 2014. Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-472279
Modern Language Association (MLA)
Sun, Guoqiang…[et al.]. Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine. Journal of Applied Mathematics No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-472279
American Medical Association (AMA)
Sun, Guoqiang& Chen, Yue& Wei, Zhinong& Li, Xiaolu& Cheung, Kwok W.. Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-472279
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-472279