Earticle

Wind power prediction has become a hotspot in recent years. The parameters relevant to the wind power are considerable and complexity. Dimension reduction has become another hotspot. The traditional methods utilize linear methods to reduce the dimension of measured data. However, data that located in high-dimensional space often have nonlinear structure. So, we consider the original data present manifold structures and introduce manifold learning methods to extract the important information. In this paper, we utilize LLE algorithm and Elman neural network to establish the wind power prediction model. The experiment results demonstrate the excellence of our method. Finally, we chose different algorithm parameters to complete the experiments and got the roughly optimal parameters. In addition, our method can be applied to similar fields.