Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction.

Shiguang Zhang,Wei Wang,Chao Liu,Baofang Chang

doi:10.3390/e22101102

Abstract

In this article, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy the single noise distribution, including Gaussian distribution and Laplace distribution, but the mixed distribution. Therefore, combining the twin hyperplanes with the fast speed of Least Squares Support Vector Regression (LS-SVR), and then introducing the Gauss–Laplace mixed noise feature, a new regressor, called Gauss-Laplace Twin Least Squares Support Vector Regression (GL-TLSSVR), for the complex noise. Subsequently, we apply the augmented Lagrangian multiplier method to solve the proposed model. Finally, we apply the short-term wind speed data-set to the proposed model. The results of this experiment confirm the effectiveness of our proposed model.

Highlights

In recent years, the support vector machine (SVM) [1,2,3,4] have received widespread attention as a powerful method, because support vector machines have better generalization performance than other machine learning techniques
The ratio of sum of squared regression (SSR)/squared deviation of testing (SST) can estimate the goodness of fit of the predictive model and extract the maximum information from the data set
Wind speed prediction is complicated by its volatility and uncertainty, so it is difficult to model with a single noise distribution

Summary

Introduction

The support vector machine (SVM) [1,2,3,4] have received widespread attention as a powerful method, because support vector machines have better generalization performance than other machine learning techniques. Based on some advantages of SVR, SVR has been successfully applied to Biology, medicine, environmental protection, information technology, engineering technology, and other fields [19,20,21,22,23,24]. In these SVR models, when solving regression problems, the noise of the training data is considered to be the single distribution. At this time, mixed noise can be well adapted to unknown or complex noise. In 2017, the research and application of a new wind speed hybrid

Methods

Discussion

Conclusion