Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting
The Kernel ridge regression (<inline-formula><math display="inline"> <semantics> <mrow> <mi>K</mi> <mi>R</mi> <mi>R</mi></mrow></semantics></math></inline-formula>) model aims to find the hidden nonlinear s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/11/2/282 |
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| author | Shiguang Zhang Ting Zhou Lin Sun Chao Liu |
| author_facet | Shiguang Zhang Ting Zhou Lin Sun Chao Liu |
| author_sort | Shiguang Zhang |
| collection | DOAJ |
| description | The Kernel ridge regression (<inline-formula><math display="inline"> <semantics> <mrow> <mi>K</mi> <mi>R</mi> <mi>R</mi></mrow></semantics></math></inline-formula>) model aims to find the hidden nonlinear structure in raw data. It makes an assumption that the noise in data satisfies the Gaussian model. However, it was pointed out that the noise in wind speed/power forecasting obeys the Beta distribution. The classic regression techniques are not applicable to this case. Hence, we derive the empirical risk loss about the Beta distribution and propose a technique of the kernel ridge regression model based on the Beta-noise (<inline-formula><math display="inline"> <semantics> <mrow> <mi>B</mi> <mi>N</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"><semantics><mrow><mi>K</mi> <mi>R</mi> <mi>R</mi></mrow></semantics></math></inline-formula>). The numerical experiments are carried out on real-world data. The results indicate that the proposed technique obtains good performance on short-term wind speed forecasting. |
| first_indexed | 2024-04-11T21:47:00Z |
| format | Article |
| id | doaj.art-ad32153bab1d41fba05cb41fb6ed976c |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T21:47:00Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-ad32153bab1d41fba05cb41fb6ed976c2022-12-22T04:01:24ZengMDPI AGSymmetry2073-89942019-02-0111228210.3390/sym11020282sym11020282Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed ForecastingShiguang Zhang0Ting Zhou1Lin Sun2Chao Liu3College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaThe State-Owned Assets Management Office, Henan Normal University, Xinxiang 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaThe Kernel ridge regression (<inline-formula><math display="inline"> <semantics> <mrow> <mi>K</mi> <mi>R</mi> <mi>R</mi></mrow></semantics></math></inline-formula>) model aims to find the hidden nonlinear structure in raw data. It makes an assumption that the noise in data satisfies the Gaussian model. However, it was pointed out that the noise in wind speed/power forecasting obeys the Beta distribution. The classic regression techniques are not applicable to this case. Hence, we derive the empirical risk loss about the Beta distribution and propose a technique of the kernel ridge regression model based on the Beta-noise (<inline-formula><math display="inline"> <semantics> <mrow> <mi>B</mi> <mi>N</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"><semantics><mrow><mi>K</mi> <mi>R</mi> <mi>R</mi></mrow></semantics></math></inline-formula>). The numerical experiments are carried out on real-world data. The results indicate that the proposed technique obtains good performance on short-term wind speed forecasting.https://www.mdpi.com/2073-8994/11/2/282ridge regression modelkernel functionBeta-noise empirical risk losswind speed forecasting |
| spellingShingle | Shiguang Zhang Ting Zhou Lin Sun Chao Liu Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting Symmetry ridge regression model kernel function Beta-noise empirical risk loss wind speed forecasting |
| title | Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting |
| title_full | Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting |
| title_fullStr | Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting |
| title_full_unstemmed | Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting |
| title_short | Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting |
| title_sort | kernel ridge regression model based on beta noise and its application in short term wind speed forecasting |
| topic | ridge regression model kernel function Beta-noise empirical risk loss wind speed forecasting |
| url | https://www.mdpi.com/2073-8994/11/2/282 |
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