Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87598
Title: Support vector regression based on grid-search method for short-term wind power forecasting
Authors: Zhang, Hong
Chen, Lixing
Qu, Yong
Zhao, Guo
Guo, Zhenwei
Keywords: Wind Power Forecasting
Support Vector Regression (SVR)
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2014
Source: Zhang, H., Chen, L., Qu, Y., Zhao, G., & Guo, Z. (2014). Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting. Journal of Applied Mathematics, 2014, 835791-. doi:10.1155/2014/835791
Series/Report no.: Journal of Applied Mathematics
Abstract: 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.
URI: https://hdl.handle.net/10356/87598
http://hdl.handle.net/10220/46771
ISSN: 1110-757X
DOI: http://dx.doi.org/10.1155/2014/835791
Rights: © 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.
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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