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Journal of Applied Mathematics
Volume 2014, Article ID 835791, 11 pages
Research Article

Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting

1School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
2Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China
3VLSI Lab, Nanyang Technological University, Singapore 639798
4School of Information Science and Engineering, Hunan University, Changsha 410082, China

Received 16 November 2013; Revised 18 April 2014; Accepted 23 April 2014; Published 17 June 2014

Academic Editor: Hongjie Jia

Copyright © 2014 Hong Zhang 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.


The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.