On Additive Gaussian Processes for Wind Farm Power Prediction

被引：0

作者：

Brealy, Simon M. ^{[1
]}

Bull, Lawrence A. ^{[2
]}

Brennan, Daniel S. ^{[1
]}

Beltrando, Pauline ^{[3
]}

Sommer, Anders ^{[3
]}

Dervilis, Nikolaos ^{[1
]}

Worden, Keith ^{[1
]}

机构：

[1] Univ Sheffield, Dept Mech Engn, Dynam Res Grp, Mappin St, Sheffield S1 3JD, England

[2] Univ Cambridge, Dept Engn, Computat Stat & Machine Learning Grp, Cambridge CB3 0FA, England

[3] Vattenfall AB, Vattenfall R&D, Alvkarleby, Sweden

来源：

PROCEEDINGS OF THE 10TH INTERNATIONAL OPERATIONAL MODAL ANALYSIS CONFERENCE, IOMAC 2024, VOL 2 | 2024年 / 515卷

基金：

英国工程与自然科学研究理事会; 英国自然环境研究理事会;

关键词：

Additive Gaussian processes; Wind power prediction; Population-based SHM;

D O I：

10.1007/978-3-031-61425-5_58

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring the use of additive Gaussian processes to reveal variations in turbine-specific and farm-level power models over a collected wind farm dataset. The predictions illustrate patterns in wind farm power generation, which follow intuition and should enable more informed control and decision-making.

引用

页码：606 / 614

页数：9

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