Noise model based v-support vector regression with its application to short-term wind speed forecasting

作者：Hu Qinghua^*; Zhang Shiguang; Xie Zongxia; Mi Jusheng; Wan Jie

来源：Neural Networks, 2014, 57: 1-11.

DOI：10.1016/j.neunet.2014.05.003

• Support vector regression
• Noise model
• Loss function
• Inequality constraints
• Wind speed forecasting

Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, 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 Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of v-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique.

• 出版日期2014-9
• 单位哈尔滨工业大学; 河北师范大学; 天津大学; 衡水学院