电测与仪表2017，Vol.54Issue(4)：1-5,99,6.
计及熵权指标及关联度排序的风电历史数据挖掘
Study on mining in the historical data of wind power based on entropy-weight index and correlation sorting
摘要
Abstract
Wind power forecasting has important significance to optimize the power dispatching plan,and promote wind power acceptance.By comparing with the historical data of load concerning the daily fluctuation characteristics and the probability distribution,the volatility of the day data of wind power has no obvious law to follow on year-on-year and week-on-week basis,and its statistics law approximately meets Weibull distribution,which undoubtedly increases the difficulty of wind power prediction technology.In order to solve the problem of useful information mining from massive wind-power historical data,an identification method of the intimate sample based on entropy-weight index and correlation sorting has been proposed,and it is applied to several common models of short-term wind power forecasting.Through case study of measured data in northern province,it shows that the proposed method has significant effects on improving the wind-power prediction accuracy and computational efficiency.关键词
风电预测/概率分布/熵权距离/关联度排序/亲密样本Key words
wind power forecasting/probability distribution/entropy-weight index/correlation sorting/intimate sample分类
信息技术与安全科学引用本文复制引用
史坤鹏,赵伟,李婷,刘梦华,王泽一..计及熵权指标及关联度排序的风电历史数据挖掘[J].电测与仪表,2017,54(4):1-5,99,6.基金项目
国家科技支撑计划项目(2015BAA01B01) （2015BAA01B01）
