LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

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 SVR of the Gaussian–Laplacian mixed homoscedastic (GLM−LSSVR) and heteroscedastic noise (GLMH−LSSVR) for complicated or unknown noise distributions. The ALM technique is used to solve model GLM−LSSVR. GLM−LSSVR 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.

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