Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine. (12th June 2014)

• Record Type: Journal Article
• Authors: Sun, Guoqiang; Chen, Yue; Wei, Zhinong; Li, Xiaolu; Cheung, Kwok W.
• Other Names: Jia Hongjie Academic Editor.
• Is Part Of: Journal of applied mathematics. Volume 2014(2014)
• Publication Date: 2014-06-12
• Subjects: Mathematics -- Periodicals; 519.05
• DOI: 10.1155/2014/437592
• Languages: English
• ISSNs: 1110-757X

Abstract

Abstract : 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.