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ν-Support Vector Regression Model Based on Gauss-Laplace Mixture Noise Characteristic for Wind Speed Prediction

by Shiguang Zhang 1,2,3,*, Ting Zhou 4, Lin Sun 1,3,*, Wei Wang 1, Chuan Wang 1 and Wentao Mao 1
1
College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
2
School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
3
Engineering Lab of Intelligence Business and Internet of Things, Xinxiang 453007, China
4
The State-Owned Assets Management Office, Henan Normal University, Xinxiang 453007, China
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(11), 1056; https://doi.org/10.3390/e21111056
Received: 19 October 2019 / Accepted: 25 October 2019 / Published: 28 October 2019
Most regression techniques assume that the noise characteristics are subject to single noise distribution whereas the wind speed prediction is difficult to model by the single noise distribution because the noise of wind speed is complicated due to its intermittency and random fluctuations. Therefore, we will present the ν -support vector regression model of Gauss-Laplace mixture heteroscedastic noise (GLM-SVR) and Gauss-Laplace mixture homoscedastic noise (GLMH-SVR) for complex noise. The augmented Lagrange multiplier method is introduced to solve models GLM-SVR and GLMH-SVR. The proposed model is applied to short-term wind speed forecasting using historical data to predict future wind speed at a certain time. The experimental results show that the proposed technique outperforms the single noise technique and obtains good performance. View Full-Text
Keywords: ν-support vector regression; Gauss-Laplace mixture noise; empirical risk loss; inequality constraints; wind speed prediction ν-support vector regression; Gauss-Laplace mixture noise; empirical risk loss; inequality constraints; wind speed prediction
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Zhang, S.; Zhou, T.; Sun, L.; Wang, W.; Wang, C.; Mao, W. ν-Support Vector Regression Model Based on Gauss-Laplace Mixture Noise Characteristic for Wind Speed Prediction. Entropy 2019, 21, 1056.

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