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Mathematical Problems in Engineering
Volume 2014, Article ID 410489, 10 pages
http://dx.doi.org/10.1155/2014/410489
Research Article

Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

1Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China
2School of Science, Hebei University of Engineering, Handan 056038, China
3School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Received 3 December 2013; Accepted 20 January 2014; Published 4 March 2014

Academic Editor: Huaiqin Wu

Copyright © 2014 Jiqiang Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction.