LAPSE

LAPSE:2019.0205
Published Article
LAPSE:2019.0205
A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction
Yiqi Chu, Chengcai Li, Yefang Wang, Jing Li, Jian Li
January 31, 2019
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.
Keywords
ensemble forecasting, statistical correction, weather classification, wind forecasting, wind power
Suggested Citation
Chu Y, Li C, Wang Y, Li J, Li J. A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction. (2019). LAPSE:2019.0205
Author Affiliations
Chu Y: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China [ORCID]
Li C: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Wang Y: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China; Baicheng Ordnance Test Center of China, Baicheng 137001, China
Li J: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Li J: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
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Journal Name
Energies
Volume
9
Issue
11
Article Number
E894
Year
2016
Publication Date
2016-10-31
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9110894, Publication Type: Journal Article
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LAPSE:2019.0205
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doi:10.3390/en9110894
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Jan 31, 2019
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CC BY 4.0
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Jan 31, 2019
 
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Calvin Tsay
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