Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting
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 e...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/835791 |
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| author | Hong Zhang Lixing Chen Yong Qu Guo Zhao Zhenwei Guo |
| author_facet | Hong Zhang Lixing Chen Yong Qu Guo Zhao Zhenwei Guo |
| author_sort | Hong Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d387c40733ae4b6f816b491ac6b6399a |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-d387c40733ae4b6f816b491ac6b6399a2025-02-03T01:33:11ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/835791835791Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power ForecastingHong Zhang0Lixing Chen1Yong Qu2Guo Zhao3Zhenwei Guo4School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, ChinaVLSI Lab, Nanyang Technological University, 639798, SingaporeSchool of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, ChinaSchool of Information Science and Engineering, Hunan University, Changsha 410082, ChinaThe 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.http://dx.doi.org/10.1155/2014/835791 |
| spellingShingle | Hong Zhang Lixing Chen Yong Qu Guo Zhao Zhenwei Guo Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting Journal of Applied Mathematics |
| title | Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting |
| title_full | Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting |
| title_fullStr | Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting |
| title_full_unstemmed | Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting |
| title_short | Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting |
| title_sort | support vector regression based on grid search method for short term wind power forecasting |
| url | http://dx.doi.org/10.1155/2014/835791 |
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