期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 3, 期 2, 页码 255-264出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2011.2180029
关键词
Radial basis function (RBF); sigmoid function; support vector machine (SVM); wavelet; wind power prediction (WPP)
资金
- National Science Foundation under CAREER Award [ECCS-0954938]
- Federal Highway Administration [DTFH61-10-H-00003]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [0954938] Funding Source: National Science Foundation
This paper proposes a wavelet support vector machine (WSVM)-based model for short-term wind power prediction (WPP). A new wavelet kernel is proposed to improve the generalization ability of the support vector machine (SVM). The proposed kernel has such a general characteristic that some commonly used kernels are its special cases. Simulation studies are carried to validate the proposed model with different prediction schemes by using the data obtained from the National Renewable Energy Laboratory (NREL). Results show that the proposed model with a fixed-step prediction scheme is preferable for short-term WPP in terms of prediction accuracy and computational cost. Moreover, the proposed model is compared with the persistence model and the SVM model with radial basis function (RBF) kernels. Results show that the proposed model not only significantly outperforms the persistence model but is also better than the RBF-SVM in terms of prediction accuracy.
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