Using a Model Structure Selection Technique to Forecast Short-Term Wind Speed for a Wind Power Plant in North China
Publication: Journal of Energy Engineering
Volume 142, Issue 1
Abstract
Model structure selection with respect to short-term wind speed forecasting is relatively difficult due to the stochastic and intermittent nature of the wind speed distribution. In order to overcome the disadvantages in traditional approaches such as computing burden and low accuracy, a novel model structure selection technique about short-term wind speed forecasting is proposed in order to improve the computational efficiency and forecasting accuracy using the model variable selection, variable order estimation, model structure optimization techniques, and so on. The detailed and complete process flow associated to the theoretical analysis of the proposed model structure selection technique is described. Moreover, both the so-called overkill in the data filtering and so-called overfitting in the learning processing are avoided by a proper technique in the design of proposed approach. In order to verify the effectiveness of proposed strategy in a practical application, all the experimental results are evaluated based on the real data provided by a sampling device with respect to a low-wind-speed wind turbine (i.e., FD-77) of a wind power plant of north China. Finally, the developed model structure selection technique is verified by the cross-validation method.
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Acknowledgments
The research reported in this paper was supported by the National Natural Science Foundation of China (Grant No. 61374006). The writers would like to thank the Editor-in-Chief Chung-Li Tseng, Associate Editor Fushuan Wen, Editorial Coordinator Holly Koppel, and three anonymous reviewers, who gave valuable comments and helpful suggestions which greatly improved the quality of the paper.
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Published In
Journal of Energy Engineering
Volume 142 • Issue 1 • March 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Jul 23, 2014
Accepted: Jan 9, 2015
Published online: Feb 10, 2015
Discussion open until: Jul 10, 2015
Published in print: Mar 1, 2016
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