Abstract
The accurate estimation of wind power is crucial not only to conduct wind energy research but also to improve policy and planning decisions for the national government. However, reliable wind speed datasets at turbine height that can be used for wind power estimation in China are very limited. In this study, the simulated wind profile (expressed by a power-law approach) is optimized using radiosonde wind speed measurements. The wind speed at a typical turbine height (80 m) is then constructed using the optimized wind profile and 10-m wind speed measured at each conventional meteorological station throughout the country. Compared with measurements, the constructed wind speed has reasonable errors (0.08 m s−1 in mean bias, 0.89 m s−1 in root mean square error, and 0.90 in correlation coefficient), which are much smaller than the model errors. Further comparison shows the constructing method in the current study outperforms the other similar statistical methods. Therefore, this method and the constructed wind speed dataset are suggested for wind energy research in China.
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Data availability
The station data were provided by the National Meteorological Information Center of the China Meteorological Administration. The ERA-Interim reanalysis data were downloaded from https://www.ecmwf.int/en/forecasts/datasets. The radiosonde data can be downloaded from the IGRA website provided. We wish to thank the China Meteorological Administration and the European Centre for Medium-Range Weather Forecasts (ECMWF) for offering the necessary data. The daily constructed 80-m wind speed data can be obtained by contacting the authors.
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Funding
This work was funded by the National Natural Science Foundation of China (Grant No. 41705084, 91537210), National Key Development Program of China (Grant No. 2018YFA0605400), and by the 13th Five-Year Information Plan of Chinese Academy of Sciences (Grant No. XXH13505-06).
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Zhou, X., Qin, J., Li, H.D. et al. A statistical method to construct wind speed at turbine height for study of wind power in China. Theor Appl Climatol 141, 419–432 (2020). https://doi.org/10.1007/s00704-020-03201-8
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DOI: https://doi.org/10.1007/s00704-020-03201-8