Advances in Science and Research
Advances in Science and Research
ASR
Articles | Volume 14
Articles | Volume 14
https://doi.org/10.5194/asr-14-227-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/asr-14-227-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 14
11 Jul 2017
 | 11 Jul 2017

Wind power application research on the fusion of the determination and ensemble prediction

Shi Lan, Xu Lina, and Hao Yuzhu

Cited articles

Baran, S.: Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components, Comput. Stat. Data An., 75, 227–238, 2014.
Deppe, A. J., Gallus Jr., W. A., and Takle, E. S.: A WRF ensemble for improved wind speed forecasts at turbine height, Weather Forecast., 28, 212–228, 2013.
Fan, L.: Preliminary study of statistically downscaled temperature ensemble predictions in eastern China, Plateau Meteorology, 29, 392–402, 2010.
Jiang, Y., Song, L., and Cheng, X.: An integrated and revised method of forecasting wind speed for wind farms, Resources Science, 35, 673–680, 2013.
Lin, W., Wang, J., Zhang, W., Guo, Z., Chi, D., and Zhang, Y.: Program of wind speed prediction based on numerical simulation with intelligent optimization algorithm, Climatic Environ. Res., 17, 646–658, 2012.
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Short summary
The fused product of wind speed for the wind farm is designed through using wind speed products of ensemble prediction from European Centre for Medium-Range Weather Forecasts and professional numerical model products on wind power based on Mesoscale Model5 and Beijing Rapid Update Cycle, which is suitable for short-term wind power forecasting and electric dispatch. The result shows that the fusion forecast has made obvious improvement on the accuracy relative to the numerical forecasting.
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