Short-Term Wind-Power Prediction Based on Wavelet Transform-Support Vector Machine and Statistic-Characteristics Analysis

The prediction algorithm is one of the most important factors in the quality of wind-power prediction. In this paper, based on the principles of wavelet transform and support vector machines (SVMs), as well as the characteristics of wind-turbine generation systems, two prediction methods are presented and discussed. In method 1, the time series of model input are decomposed into different frequency modes, and the models are set up separately based on the SVM theory. The results are combined together to forecast the final wind-power output. For comparison purposes, the wavelet kernel function is applied in place of the radial basis function (RBF) kernel function during SVM training in method 2. The operation data of one wind farm from Texas are used. Mean relative error and relative mean square error are used to evaluate the forecasting errors of the two proposed methods and the RBF SVM model. The means of evaluating the prediction-algorithm precision is also proposed.