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
收藏
页码：606 / 614
页数：9
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