Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting
Joint Authors
Chen, Lixing
Zhao, Guo
Qu, Yong
Guo, Zhenwei
Zhang, Hong
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Hong& Chen, Lixing& Qu, Yong& Zhao, Guo& Guo, Zhenwei. 2014. Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-501951
Modern Language Association (MLA)
Zhang, Hong…[et al.]. Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting. Journal of Applied Mathematics No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-501951
American Medical Association (AMA)
Zhang, Hong& Chen, Lixing& Qu, Yong& Zhao, Guo& Guo, Zhenwei. Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-501951
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-501951