气候变化领域集成服务门户(CCPortal): Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method

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DOI	10.1016/j.atmosres.2019.04.011
	Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method
	Di Z.; Ao J.; Duan Q.; Wang J.; Gong W.; Shen C.; Gan Y.; Liu Z.
发表日期	2019
ISSN	0169-8095
起始页码	1
结束页码	16
卷号	226
英文摘要	Improving turbine-height wind-speed forecasting using a mesoscale numerical weather prediction (NWP) model is important for wind-power prediction because of the cubic correlation between wind power and wind speed. This study investigates how a surrogate-based automatic optimization method can be used to improve wind-speed forecasting by an NWP model by optimizing its parameters. A key challenge in optimizing NWP model parameters is the tremendous computational requirements of such an exercise. A global sensitivity method known as the Multivariate Adaptive Regression Spline (MARS) method was first used to identify the most sensitive parameters among all tunable parameters chosen from seven physical parameterization schemes of the Weather Research and Forecast (WRF) model. Then, a highly effective and efficient optimization method known as adaptive surrogate modeling-based optimization (ASMO) was used to tune the sensitive parameters. In a case study carried out over Eastern China, the seven parameters that were most sensitive to wind-speed simulation were identified from among 27 tunable parameters. Those seven parameters were optimized using the ASMO method. The present study indicates that the hourly wind-speed simulation accuracy was improved by 8.7% during the calibration phase and by 7.58% during the validation phase. In addition, clear physical interpretations were provided to explain why the optimal parameters lead to improved wind speed forecasts. Overall, this study has demonstrated that automatic optimization method is a highly effective and efficient way to improve wind-speed forecasting by an NWP model. © 2019
英文关键词	Parameter optimization; Surrogate modeling-based optimization; Turbine-height wind-speed forecasting; WRF
语种	英语
scopus关键词	Speed; Turbines; Wind; Wind power; Computational requirements; Multivariate adaptive regression splines; Numerical weather prediction models; Parameter optimization; Physical parameterization; Surrogate model; Weather Research and Forecast models; Wind speed forecasting; Weather forecasting; computer simulation; forecasting method; numerical model; optimization; parameterization; surrogate method; turbine; wind velocity; China
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162050
作者单位	State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd., Beijing, 100176, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
推荐引用方式 GB/T 7714	Di Z.,Ao J.,Duan Q.,et al. Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method[J],2019,226.
APA	Di Z..,Ao J..,Duan Q..,Wang J..,Gong W..,...&Liu Z..(2019).Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method.Atmospheric Research,226.
MLA	Di Z.,et al."Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method".Atmospheric Research 226(2019).