Review on Wind Speed Prediction Based on Spatial Correlation

被引：0

作者：

Xue Y. ^{[1
,2
]}

Chen N. ^{[3
,4
]}

Wang S. ^{[5
]}

Wen F. ^{[6
,7
]}

Lin Z. ^{[6
]}

Wang Z. ^{[6
]}

机构：

[1] NARI Group Corporation, State Grid Electric Power Research Institute, Nanjing

[2] State Key Laboratory of Smart Grid Protection and Control, Nanjing

[3] School of Electrical Engineering, Southeast University, Nanjing

[4] State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Nanjing

[5] China Shenhua Energy Company Limited, Beijing

[6] School of Electrical Engineering, Zhejiang University, Hangzhou

[7] Department of Electrical & Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2017年 / 41卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Dynamic features; Offline modeling by classification; Online feature matching; Spatial correlation; Wind speed prediction;

D O I：

10.7500/AEPS20170109002

中图分类号：

学科分类号：

摘要：

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. © 2017 Automation of Electric Power Systems Press.

引用

页码：161 / 169

页数：8

共 66 条

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