A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimat...
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Autor*in:	Yiqi Chu [verfasserIn] Chengcai Li [verfasserIn] Yefang Wang [verfasserIn] Jing Li [verfasserIn] Jian Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	wind power wind forecasting statistical correction weather classification ensemble forecasting

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 9(2016), 11, p 894
Übergeordnetes Werk:	volume:9 ; year:2016 ; number:11, p 894

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en9110894

Katalog-ID:	DOAJ084974087

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allfieldsGer	10.3390/en9110894 doi (DE-627)DOAJ084974087 (DE-599)DOAJ74aa30fe0a3646419078867f10a60445 DE-627 ger DE-627 rakwb eng Yiqi Chu verfasserin aut A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction. wind power wind forecasting statistical correction weather classification ensemble forecasting Technology T Chengcai Li verfasserin aut Yefang Wang verfasserin aut Jing Li verfasserin aut Jian Li verfasserin aut In Energies MDPI AG, 2008 9(2016), 11, p 894 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:9 year:2016 number:11, p 894 https://doi.org/10.3390/en9110894 kostenfrei https://doaj.org/article/74aa30fe0a3646419078867f10a60445 kostenfrei http://www.mdpi.com/1996-1073/9/11/894 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2016 11, p 894
allfieldsSound	10.3390/en9110894 doi (DE-627)DOAJ084974087 (DE-599)DOAJ74aa30fe0a3646419078867f10a60445 DE-627 ger DE-627 rakwb eng Yiqi Chu verfasserin aut A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction. wind power wind forecasting statistical correction weather classification ensemble forecasting Technology T Chengcai Li verfasserin aut Yefang Wang verfasserin aut Jing Li verfasserin aut Jian Li verfasserin aut In Energies MDPI AG, 2008 9(2016), 11, p 894 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:9 year:2016 number:11, p 894 https://doi.org/10.3390/en9110894 kostenfrei https://doaj.org/article/74aa30fe0a3646419078867f10a60445 kostenfrei http://www.mdpi.com/1996-1073/9/11/894 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2016 11, p 894
language	English
source	In Energies 9(2016), 11, p 894 volume:9 year:2016 number:11, p 894
sourceStr	In Energies 9(2016), 11, p 894 volume:9 year:2016 number:11, p 894
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	wind power wind forecasting statistical correction weather classification ensemble forecasting Technology T
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author	Yiqi Chu
spellingShingle	Yiqi Chu misc wind power misc wind forecasting misc statistical correction misc weather classification misc ensemble forecasting misc Technology misc T A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction
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topic_title	A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction wind power wind forecasting statistical correction weather classification ensemble forecasting
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title	A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction
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title_auth	A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction
abstract	Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.
abstractGer	Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.
abstract_unstemmed	Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.
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A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

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