Adaptabilities of three mainstream short-term wind power forecasting methods

Variability and intermittency of wind is the main challenge for making a reliable wind power forecasting (WPF). Meteorological and topological complexities make it even harder to fit any forecasting algorithm to one particular case. This paper presents the comparison of three short term WPF models based on three wind farms in China with different terrains and climates. The sensitivity effects of training samples on forecasting performance are investigated in terms of sample size, sample quality, and sample time scale. Then, their adaptabilities and modeling efficiency are also discussed under different seasonal and topographic conditions. Results show that (1) radial basis function (RBF) and support vector machine (SVM) generally have higher prediction accuracy than that of genetic algorithm back propagation (GA-BP), but different models show advantages in different seasons and terrains. (2) WPF taking a month as the training time interval can increase the accuracy of short-term WPF. (3) The change of sample number for the GA-BP and RBF is less sensitive than that of the SVM. (4) GA-BP forecasting accuracy is equally sensitive to all size of training samples. RBF and SVM have different sensibility to different size of training samples. This study can quantitatively provide reference for choosing the appropriate WPF model and further optimization for specific engineering cases, based on better understanding of algorithm theory and its adaptability. In this way, WPF users can select the suitable algorithm for different terrains and climates to achieve reliable prediction for market clearing, efficient pricing, dispatching, etc.