电网技术2018，Vol.42Issue(3)：842-848,7.DOI:10.13335/j.1000-3673.pst.2017.1200
基于优化FCM聚类的RELM风速预测
Wind Speed Forecasting of Regularized ELM Based on Optimized FCM Clustering
摘要
Abstract
Accurate wind speed forecasting is of great significance for large-scale wind power integration. In this paper, a new method of short-term wind speed forecasting is put forward based on regularized extreme learning machine (ELM) of optimal clustering and mutual information attribute reduction. Firstly, considering different effects of different attributes on wind speed, mutual information between wind speed characteristic sequence and wind speed sequence are calculated. The attribute features are selected with maximum- correlation minimum-redundancy algorithm. Then, wind samples are clustered with optimized fuzzy C-means (FCM) clustering method. The ELM is optimized and a combined forecasting model of wind speed is constructed. Finally, wind speed prediction experiment is carried out combined with measured data of wind farm. Results show that the method has high prediction accuracy.关键词
风速预测/最大相关最小冗余/模糊C均值聚类/正则化/极限学习机Key words
wind speed forecasting/minimal redundancy maximal relevance/fuzzy C-means clustering/regularization/extreme learning machine分类
信息技术与安全科学引用本文复制引用
潘超,秦本双,何瑶,袁翀,沈清野..基于优化FCM聚类的RELM风速预测[J].电网技术,2018,42(3):842-848,7.基金项目
国家863高技术基金项目(SS2014AA052502) （SS2014AA052502）
国家自然科学基金项目(51507027). Project Supported by the National High Technology Research and Development Program of China (863 Program) (SS2014AA052502) （51507027）
National Natural Science Foundation of China (51507027). （51507027）
