文章摘要
薛禹胜,陈宁,王树民,等.关于利用空间相关性预测风速的评述[J].电力系统自动化,2017,41(10):161-169. DOI: 10.7500/AEPS20170109002.
XUE Yusheng,CHEN Ning,WANG Shumin, et al.Review on Wind Speed Prediction Based on Spatial Correlation[J].Automation of Electric Power Systems,2017,41(10):161-169. DOI: 10.7500/AEPS20170109002.
关于利用空间相关性预测风速的评述
Review on Wind Speed Prediction Based on Spatial Correlation
DOI:10.7500/AEPS20170109002
关键词: 风速预测  空间相关性  动态特征  离线分类建模  在线特征匹配
KeyWords: wind speed prediction  spatial correlation  dynamic features  offline modeling by classification  online feature matching
上网日期:2017-03-28
基金项目:国家自然科学基金重点项目(61533010);NSFC-NRCT(中泰)合作研究项目(51561145011);国家电网公司科技项目
作者单位E-mail
薛禹胜 南瑞集团公司(国网电力科学研究院), 江苏省南京市 211106; 智能电网保护和运行控制国家重点实验室, 江苏省南京市 211106 xueyusheng@sgepri.sgcc.com.cn 
陈宁 东南大学电气工程学院, 江苏省南京市 210096; 新能源与储能运行控制国家重点实验室(中国电力科学研究院), 江苏省南京市 210003  
王树民 中国神华能源股份有限公司, 北京市100011  
文福拴 浙江大学电气工程学院, 浙江省杭州市 310027; 文莱科技大学电机与电子工程系, 斯里巴加湾 BE1410, 文莱  
林振智 浙江大学电气工程学院, 浙江省杭州市 310027  
汪震 浙江大学电气工程学院, 浙江省杭州市 310027  
摘要:
      归纳了空间相关性风速预测的现状;引入条件相关性及相应的可信相关度概念,以代替常规的相关性;基于大数据思维,提出将数据驱动与因果驱动相结合的预测框架。从历史数据中挖掘相关性,利用空间相关性增加风速预测的数据源,部分克服历史数据缺失的困难;利用大时滞的空间相关性,有助于预测下游风速的突变。最后,依托该框架展望了空间相关性风速预测的前景。
Abstract:
      The state-of-the-art development of spatial correlation based wind speed prediction is reviewed. And the concepts of conditional correlation and its corresponding confidence correlation are introduced to improve traditional spatial correlation. Based on big-data thinking, a framework of integrating data-driven with causality-driven wind speed prediction is proposed. In the framework, correlation is mined from historical data for wind speed prediction. Spatial correlation is employed to import data sources for wind speed prediction to overcome the shortage of historical data in part. Furthermore, spatial correlation with long time lag can be used to predict drastic and sudden change in downstream wind speed. Finally, suggestions for future research under the proposed framework can be made with confidence. This work is supported by the State Key Program of National Natural Science Foundation of China(No. 61533010), NSFC-NRCT(Sino Thai)Cooperation Research Project(No. 51561145011)and State Grid Corporation of China.
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