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

• Published in: Entropy (Basel, Switzerland) Vol. 21; no. 11; p. 1056
• Main Authors: Zhang, Shiguang, Zhou, Ting, Sun, Lin, Wang, Wei, Wang, Chuan, Mao, Wentao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2019; MDPI
• Subjects: Algorithms; Approximation; Datasets; empirical risk loss; Forecasting; gauss-laplace mixture noise; inequality constraints; Lagrange multiplier; Neural networks; Noise; Noise prediction; Optimization; Regression models; Standard deviation; Support vector machines; Wind power; Wind speed; wind speed prediction; ν-support vector regression
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e21111056

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.