A method for short-term wind power prediction with multiple observation points

Muhammad Khalid; Andrey V. Savkin

doi:10.1109/TPWRS.2011.2160295

A method for short-term wind power prediction with multiple observation points

Muhammad Khalid^*
, Andrey V. Savkin

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

163 Scopus citations

Abstract

This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.

Original language	English
Article number	6144039
Pages (from-to)	579-586
Number of pages	8
Journal	IEEE Transactions on Power Systems
Volume	27
Issue number	2
DOIs	https://doi.org/10.1109/TPWRS.2011.2160295
State	Published - May 2012
Externally published	Yes

Bibliographical note

Funding Information:
Manuscript received June 03, 2009; revised January 31, 2010 and June 22, 2010; accepted August 12, 2010. Date of publication January 31, 2012; date of current version April 18, 2012. This work was supported in part by the Australian Research Council. Paper no. TPWRS-00416-2009.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Adaptive filtering
networked systems
prediction
renewable energy
wind power

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

Access to Document

10.1109/TPWRS.2011.2160295

Cite this

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title = "A method for short-term wind power prediction with multiple observation points",

abstract = "This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.",

keywords = "Adaptive filtering, networked systems, prediction, renewable energy, wind power",

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note = "Funding Information: Manuscript received June 03, 2009; revised January 31, 2010 and June 22, 2010; accepted August 12, 2010. Date of publication January 31, 2012; date of current version April 18, 2012. This work was supported in part by the Australian Research Council. Paper no. TPWRS-00416-2009.",

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N2 - This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.

AB - This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.

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KW - networked systems

KW - prediction

KW - renewable energy

KW - wind power

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A method for short-term wind power prediction with multiple observation points

Abstract

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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