Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression

Abstract

Wind speed or wind power forecasting plays an important role in large-scale wind power penetration due to their uncertainty. Support vector regression, widely used in wind speed or wind power forecasting, aims at discovering natural structures of wind variation hidden in historical data. Most current regression algorithms, including least squares support vector regression (SVR), assume that the noise of the data is Gaussian with zero mean and the same variance. However, it is discovered that the uncertainty of short-term wind speed satisfies Gaussian distribution with zero mean and heteroscedasticity in this work. This kind of task is called heteroscedastic regression. In order to deal with this problem, we derive an optimal loss function for heteroscedastic regression and develop a new framework of $\nu$ -SVR for learning tasks of Gaussian noise (GN) with heteroscedasticity. In addition, we introduce the stochastic gradient descent (SGD) method to solve the proposed model, which leads the models to be trained online. Finally, we reveal the uncertainty properties of wind speed with two real-world datasets and test the proposed algorithms on these data. The experimental results confirm the effectiveness of the proposed model.