System bias correction of short-term hub-height wind forecasts using the Kalman filter

Abstract Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model...

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Autores principales:	Jingjing Xu, Ziniu Xiao, Zhaohui Lin, Ming Li
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Wind forecasts Wind energy Numerical model Bias correction Kalman filter Atmospheric boundary layer Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/141c7bb72bb24f6b9152530ac42048e9
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spelling	oai:doaj.org-article:141c7bb72bb24f6b9152530ac42048e92021-11-21T12:40:06ZSystem bias correction of short-term hub-height wind forecasts using the Kalman filter10.1186/s41601-021-00214-x2367-26172367-0983https://doaj.org/article/141c7bb72bb24f6b9152530ac42048e92021-11-01T00:00:00Zhttps://doi.org/10.1186/s41601-021-00214-xhttps://doaj.org/toc/2367-2617https://doaj.org/toc/2367-0983Abstract Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model in Zhangbei wind farm for a period over two years. The KF approach shows a remarkable ability in improving the raw forecasts by decreasing the root-mean-square error by 16% from 3.58 to 3.01 m s−1, the mean absolute error by 14% from 2.71 to 2.34 m s−1, the bias from 0.22 to − 0.19 m s−1, and improving the correlation from 0.58 to 0.66. The KF significantly reduces random errors of the model, showing the capability to deal with the forecast errors associated with physical processes which cannot be accurately handled by the numerical model. In addition, the improvement of the bias correction is larger for wind speeds sensitive to wind power generation. So the KF approach is suitable for short-term wind power prediction.Jingjing XuZiniu XiaoZhaohui LinMing LiSpringerOpenarticleWind forecastsWind energyNumerical modelBias correctionKalman filterAtmospheric boundary layerDistribution or transmission of electric powerTK3001-3521Production of electric energy or power. Powerplants. Central stationsTK1001-1841ENProtection and Control of Modern Power Systems, Vol 6, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wind forecasts Wind energy Numerical model Bias correction Kalman filter Atmospheric boundary layer Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	Wind forecasts Wind energy Numerical model Bias correction Kalman filter Atmospheric boundary layer Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841 Jingjing Xu Ziniu Xiao Zhaohui Lin Ming Li System bias correction of short-term hub-height wind forecasts using the Kalman filter
description	Abstract Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model in Zhangbei wind farm for a period over two years. The KF approach shows a remarkable ability in improving the raw forecasts by decreasing the root-mean-square error by 16% from 3.58 to 3.01 m s−1, the mean absolute error by 14% from 2.71 to 2.34 m s−1, the bias from 0.22 to − 0.19 m s−1, and improving the correlation from 0.58 to 0.66. The KF significantly reduces random errors of the model, showing the capability to deal with the forecast errors associated with physical processes which cannot be accurately handled by the numerical model. In addition, the improvement of the bias correction is larger for wind speeds sensitive to wind power generation. So the KF approach is suitable for short-term wind power prediction.
format	article
author	Jingjing Xu Ziniu Xiao Zhaohui Lin Ming Li
author_facet	Jingjing Xu Ziniu Xiao Zhaohui Lin Ming Li
author_sort	Jingjing Xu
title	System bias correction of short-term hub-height wind forecasts using the Kalman filter
title_short	System bias correction of short-term hub-height wind forecasts using the Kalman filter
title_full	System bias correction of short-term hub-height wind forecasts using the Kalman filter
title_fullStr	System bias correction of short-term hub-height wind forecasts using the Kalman filter
title_full_unstemmed	System bias correction of short-term hub-height wind forecasts using the Kalman filter
title_sort	system bias correction of short-term hub-height wind forecasts using the kalman filter
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/141c7bb72bb24f6b9152530ac42048e9
work_keys_str_mv	AT jingjingxu systembiascorrectionofshorttermhubheightwindforecastsusingthekalmanfilter AT ziniuxiao systembiascorrectionofshorttermhubheightwindforecastsusingthekalmanfilter AT zhaohuilin systembiascorrectionofshorttermhubheightwindforecastsusingthekalmanfilter AT mingli systembiascorrectionofshorttermhubheightwindforecastsusingthekalmanfilter
_version_	1718418868050853888

System bias correction of short-term hub-height wind forecasts using the Kalman filter

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