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

摘要：

　　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.

关键词： Wind power prediction wind type feature weighted fuzzy clustering Elman neural network

作者: Xinkun Wang Diansheng Luo Hongying He

作者单位: College of Electrical Information and Engineering,Hunan University,Changsha 410082,Hunan,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 575-584

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）