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

作　　者：薛禹胜[1,2] 陈宁[3,4] 王树民[5] 文福拴[6,7] 林振智[6] 汪震[6] 

机构地区：[1]南瑞集团公司(国网电力科学研究院),江苏省南京市211106 [2]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 [3]东南大学电气工程学院,江苏省南京市210096 [4]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 [5]中国神华能源股份有限公司,北京市100011 [6]浙江大学电气工程学院,浙江省杭州市310027 [7]文莱科技大学电机与电子工程系

出　　处：《电力系统自动化》2017年第10期161-169,共9页Automation of Electric Power Systems

基　　金：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~~

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。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.

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 

分 类 号：TM614[电气工程—电力系统及自动化]