Ultra-short term wind speed prediction based on spatial correlation by k-nearest neighbor

被引：0

作者：

Yang Z. ^{[1
]}

Zhao Q. ^{[1
]}

Wu B. ^{[1
]}

Hou J. ^{[1
]}

Chen X. ^{[1
]}

Zhang J. ^{[2
]}

机构：

[1] School of Electrical and Information Engineering, Tianjin University, Tianjin

[2] Key Laboratory of Process Measurement and Control, Tianjin University, Tianjin

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2019年 / 39卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Historical observations; K-nearest neighbor; Spatial correlation; Ultra-short term; Wind speed prediction;

D O I：

10.16081/j.issn.1006-6047.2019.03.028

中图分类号：

学科分类号：

摘要：

One approach to improve the accuracy and reliability of ultra-short term wind speed prediction is to tho-roughly exploit the characteristics and laws of wind speed correlation from historical observations. The reference vector of k-nearest neighbor prediction based on spatial correlation is formed by the combination of latest local wind speed historical observations and upstream wind speed observations adjusted by its optimal lag time. The correlation coefficient is taken as the concrete evaluation index, and k most similar neighbours of the reference vector are optimally selected from the wind speed historical observations. Seven regression models are adopted for the future local wind speed prediction. The simulative results of wind speed prediction of Huibertgat, Holland in winter show that the optimal number of k-nearest neighbours is about 100 and the optimal year number of historical data is 10 a by the prediction of three optimal models, i. e. linear regression, partial least squares regression and least squares support vector machine regression, and the proposed method can effectively use the similarity of historical data for reliable ultrashort term wind speed prediction. © 2019, Electric Power Automation Equipment Press. All right reserved.

引用

页码：175 / 181

页数：6

共 20 条

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