Spatial prediction of wind farm outputs using the Augmented Kriging-based Model

Jin Hur; Ross Baldick

doi:10.1109/PESGM.2012.6345117

Spatial prediction of wind farm outputs using the Augmented Kriging-based Model

Jin Hur, Ross Baldick

Department of Climate and Energy Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Wind generating resources have been increasing more rapidly than any other renewable generating resources. Wind farm output prediction is an important issue for deploying higher wind power penetrations on power grids. The existing work on wind farm output prediction has focused on the temporal issues. As wind farm outputs depend on natural wind resources that vary over space and time, spatial analysis and modeling is also needed. Predictions about suitability for locating new wind generating resources can be performed by optimal spatial modeling. In this paper, a new approach to spatial prediction of wind farm outputs is proposed using the Augmented Kriging-based Model (AKM).

Original language	English
Title of host publication	2012 IEEE Power and Energy Society General Meeting, PES 2012
DOIs	https://doi.org/10.1109/PESGM.2012.6345117
State	Published - 2012
Event	2012 IEEE Power and Energy Society General Meeting, PES 2012 - San Diego, CA, United States Duration: 22 Jul 2012 → 26 Jul 2012

Publication series

Name	IEEE Power and Energy Society General Meeting
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2012 IEEE Power and Energy Society General Meeting, PES 2012
Country/Territory	United States
City	San Diego, CA
Period	22/07/12 → 26/07/12

Keywords

Augmented Kriging-based Model (AKM)
Spatial Prediction
Universal Kriging (UK)
Wind Generating Resources

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/PESGM.2012.6345117

Cite this

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title = "Spatial prediction of wind farm outputs using the Augmented Kriging-based Model",

abstract = "Wind generating resources have been increasing more rapidly than any other renewable generating resources. Wind farm output prediction is an important issue for deploying higher wind power penetrations on power grids. The existing work on wind farm output prediction has focused on the temporal issues. As wind farm outputs depend on natural wind resources that vary over space and time, spatial analysis and modeling is also needed. Predictions about suitability for locating new wind generating resources can be performed by optimal spatial modeling. In this paper, a new approach to spatial prediction of wind farm outputs is proposed using the Augmented Kriging-based Model (AKM).",

keywords = "Augmented Kriging-based Model (AKM), Spatial Prediction, Universal Kriging (UK), Wind Generating Resources",

author = "Jin Hur and Ross Baldick",

year = "2012",

doi = "10.1109/PESGM.2012.6345117",

language = "English",

isbn = "9781467327275",

series = "IEEE Power and Energy Society General Meeting",

booktitle = "2012 IEEE Power and Energy Society General Meeting, PES 2012",

note = "2012 IEEE Power and Energy Society General Meeting, PES 2012 ; Conference date: 22-07-2012 Through 26-07-2012",

}

Hur, J & Baldick, R 2012, Spatial prediction of wind farm outputs using the Augmented Kriging-based Model. in 2012 IEEE Power and Energy Society General Meeting, PES 2012., 6345117, IEEE Power and Energy Society General Meeting, 2012 IEEE Power and Energy Society General Meeting, PES 2012, San Diego, CA, United States, 22/07/12. https://doi.org/10.1109/PESGM.2012.6345117

TY - GEN

T1 - Spatial prediction of wind farm outputs using the Augmented Kriging-based Model

AU - Hur, Jin

AU - Baldick, Ross

PY - 2012

Y1 - 2012

N2 - Wind generating resources have been increasing more rapidly than any other renewable generating resources. Wind farm output prediction is an important issue for deploying higher wind power penetrations on power grids. The existing work on wind farm output prediction has focused on the temporal issues. As wind farm outputs depend on natural wind resources that vary over space and time, spatial analysis and modeling is also needed. Predictions about suitability for locating new wind generating resources can be performed by optimal spatial modeling. In this paper, a new approach to spatial prediction of wind farm outputs is proposed using the Augmented Kriging-based Model (AKM).

AB - Wind generating resources have been increasing more rapidly than any other renewable generating resources. Wind farm output prediction is an important issue for deploying higher wind power penetrations on power grids. The existing work on wind farm output prediction has focused on the temporal issues. As wind farm outputs depend on natural wind resources that vary over space and time, spatial analysis and modeling is also needed. Predictions about suitability for locating new wind generating resources can be performed by optimal spatial modeling. In this paper, a new approach to spatial prediction of wind farm outputs is proposed using the Augmented Kriging-based Model (AKM).

KW - Augmented Kriging-based Model (AKM)

KW - Spatial Prediction

KW - Universal Kriging (UK)

KW - Wind Generating Resources

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U2 - 10.1109/PESGM.2012.6345117

DO - 10.1109/PESGM.2012.6345117

M3 - Conference contribution

AN - SCOPUS:84870583667

SN - 9781467327275

T3 - IEEE Power and Energy Society General Meeting

BT - 2012 IEEE Power and Energy Society General Meeting, PES 2012

T2 - 2012 IEEE Power and Energy Society General Meeting, PES 2012

Y2 - 22 July 2012 through 26 July 2012

ER -

Spatial prediction of wind farm outputs using the Augmented Kriging-based Model

Abstract

Publication series

Conference

Keywords

UN SDGs

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