摘要
讨论超短期风电功率预测(USTWPP)模型的适用性。提出的USTWPP方法,从历史数据的风电功率时间序列(WPTS)中筛选特征量,选择门限值,并将短窗口内的WPTS划分为不同形态的子集,以及一个囊括所有不具有排他性分类特征的"非形态子集"。然后在离线环境下,分别按对应的训练样本优化各子集专用的预测模型及参数。在线应用时,将当前时刻前一个短窗口的WPTS与各子集的分类判据比对,以归入上述子集之一,然后调用相应的预测模型完成USTWPP。最后,以实际算例验证了该方法的有效性。
The applicability of ultra-short-term wind power prediction(USTWPP) models is reviewed.The USTWPP method proposed extracts featrues from historical data of wind power time series(WPTS),and classifies every short WPTS into one of several different subsets well defined by stationary patterns.All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern.Every above WPTS subset needs a USTWPP model specially optimized for it offline.For on-line application,the pattern of the last short WPTS is recognized,then the corresponding prediction model is called for USTWPP.The validity of the proposed method is verified by simulations.
出处
《电力系统自动化》
EI
CSCD
北大核心
2015年第8期5-11,共7页
Automation of Electric Power Systems
基金
国家重点基础研究发展计划(973计划)资助项目(2013CB228204)
澳大利亚ARC项目(DP120101345)
中英合作研究项目(NSFC-513111025-2013
EPSRC-EP/L001063/1)
国家电网公司科技项目~~
关键词
风电预测
时间序列特征
序列趋势分类
离线优化
在线匹配
wind power prediction
time series features
classification of time-series tendency
offline optimization
online matchin
