Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian-Laplacian mixed homoscedastic ( G L M - L S S V R ) and heteroscedastic noise ( G L M H - L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M - L S S V R . G L M - L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.
Keywords: Gaussian–Laplacian mixed noise-characteristic; Least square SVR; empirical risk loss; equality constraint; wind-speed forecasting.