An Improved Feature Weighted Fuzzy Clustering Algorithm with Its Application in Short-Term Prediction of Wind Power

Based on improved feature weighted fuzzy clustering and Elman neural network, short-term forecasting method of wind power is proposed in the paper. Because physical properties of wind identify wind types with different importance, the paper introduces weighted factor in traditional FCM fuzzy clustering algorithm and synthetically clusters the data samples of historical wind type. Aim at clustering results, it dynamically establishes model of Elman neural network in order to predict wind power output value of the same clustering results in target day. Furthermore, the paper simulates experiments with measured data of a domestic wind field, which proves the superiority and practicability of the proposed method.