Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs

Francisco Javier Jara Avila; Timothy Verstraeten; Konstantinos Vratsinis; Ann Nowe; Jan Helsen

doi:10.1088/1742-6596/2505/1/012035

Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs

Francisco Javier Jara Avila, Timothy Verstraeten, Konstantinos Vratsinis, Ann Nowe, Jan Helsen

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

3 Citations (Scopus)

Abstract

Wind is a renewable energy source that has become more important in recent years. Wind turbines are equipped with a SCADA system, which allows for remote supervision of the wind farm. SCADA systems are customarily used to provide data averaged every 10 minutes. Nevertheless, recent literature suggests that more insights could be extracted with a higher granularity of data. In this work, a naive methodology based on Multi-Task Gaussian Process Regression is presented, in order to show how spatiotemporal modeling benefits power estimation. Using sparsity properties a model for possible power prediction is proposed. The model proposed performs better than the power curves provided by the manufacturer.

Original language	English
Title of host publication	Journal of Physics: Conference Series
Place of Publication	Visby, Sweden
Publisher	IOP Publishing
Number of pages	12
Volume	2505
Edition	1
DOIs	https://doi.org/10.1088/1742-6596/2505/1/012035
Publication status	Published - 22 Jun 2023

Publication series

Name	Journal of Physics: Conference Series
ISSN (Print)	1742-6588

Bibliographical note

Funding Information:
This research was funded in the context of the Energy Transition Fund project Poseidon. The authors acknowledge VLAIO/Blue Cluster Supersized 4.0 ICON project (HBC.2019.0135), ICON Rainbow project, and SEAFD project. The research was supported by the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program. The authors are also grateful to the VSC Supercomputing Flanders centre for the support in the context of the VSC Cloud program.

Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

Keywords

Machine Learning
Wind Energy
Gaussian Process

Access to Document

10.1088/1742-6596/2505/1/012035

Cite this

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title = "Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs",

abstract = "Wind is a renewable energy source that has become more important in recent years. Wind turbines are equipped with a SCADA system, which allows for remote supervision of the wind farm. SCADA systems are customarily used to provide data averaged every 10 minutes. Nevertheless, recent literature suggests that more insights could be extracted with a higher granularity of data. In this work, a naive methodology based on Multi-Task Gaussian Process Regression is presented, in order to show how spatiotemporal modeling benefits power estimation. Using sparsity properties a model for possible power prediction is proposed. The model proposed performs better than the power curves provided by the manufacturer.",

keywords = "Machine Learning, Wind Energy, Gaussian Process",

author = "{Jara Avila}, {Francisco Javier} and Timothy Verstraeten and Konstantinos Vratsinis and Ann Nowe and Jan Helsen",

note = "Funding Information: This research was funded in the context of the Energy Transition Fund project Poseidon. The authors acknowledge VLAIO/Blue Cluster Supersized 4.0 ICON project (HBC.2019.0135), ICON Rainbow project, and SEAFD project. The research was supported by the Flemish Government under the “Onderzoeksprogramma Artifici{\"e}le Intelligentie (AI) Vlaanderen” program. The authors are also grateful to the VSC Supercomputing Flanders centre for the support in the context of the VSC Cloud program. Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.",

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Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs. / Jara Avila, Francisco Javier ; Verstraeten, Timothy ; Vratsinis, Konstantinos et al.
Journal of Physics: Conference Series. Vol. 2505 1. ed. Visby, Sweden: IOP Publishing, 2023. (Journal of Physics: Conference Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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AU - Jara Avila, Francisco Javier

AU - Verstraeten, Timothy

AU - Vratsinis, Konstantinos

AU - Nowe, Ann

AU - Helsen, Jan

N1 - Funding Information: This research was funded in the context of the Energy Transition Fund project Poseidon. The authors acknowledge VLAIO/Blue Cluster Supersized 4.0 ICON project (HBC.2019.0135), ICON Rainbow project, and SEAFD project. The research was supported by the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program. The authors are also grateful to the VSC Supercomputing Flanders centre for the support in the context of the VSC Cloud program. Publisher Copyright: © Published under licence by IOP Publishing Ltd. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.

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AB - Wind is a renewable energy source that has become more important in recent years. Wind turbines are equipped with a SCADA system, which allows for remote supervision of the wind farm. SCADA systems are customarily used to provide data averaged every 10 minutes. Nevertheless, recent literature suggests that more insights could be extracted with a higher granularity of data. In this work, a naive methodology based on Multi-Task Gaussian Process Regression is presented, in order to show how spatiotemporal modeling benefits power estimation. Using sparsity properties a model for possible power prediction is proposed. The model proposed performs better than the power curves provided by the manufacturer.

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KW - Wind Energy

KW - Gaussian Process

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T3 - Journal of Physics: Conference Series

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ER -

Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs

Abstract

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Keywords

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