Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2019.04.011 |
Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method | |
Di, Zhenhua1; Ao, Juan2; Duan, Qingyun1; Wang, Jin2; Gong, Wei1; Shen, Chenwei1; Gan, Yanjun3; Liu, Zhao2 | |
2019-09-15 | |
发表期刊 | ATMOSPHERIC RESEARCH
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ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2019 |
卷号 | 226页码:1-16 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
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. |
英文关键词 | WRF Parameter optimization Surrogate modeling-based optimization Turbine-height wind-speed forecasting |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000469904100001 |
WOS关键词 | PLANETARY BOUNDARY-LAYER ; CONVECTIVE PARAMETERIZATION ; SENSITIVITY-ANALYSIS ; WEATHER RESEARCH ; PBL SCHEMES ; ENSEMBLE ; SYSTEM ; SIMULATION |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/187030 |
专题 | 地球科学 |
作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China; 2.Beijing Goldwind Sci & Creat Windpower Equipment, Beijing 100176, Peoples R China; 3.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China |
推荐引用方式 GB/T 7714 | Di, Zhenhua,Ao, Juan,Duan, Qingyun,et al. Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method[J]. ATMOSPHERIC RESEARCH,2019,226:1-16. |
APA | Di, Zhenhua.,Ao, Juan.,Duan, Qingyun.,Wang, Jin.,Gong, Wei.,...&Liu, Zhao.(2019).Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method.ATMOSPHERIC RESEARCH,226,1-16. |
MLA | Di, Zhenhua,et al."Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method".ATMOSPHERIC RESEARCH 226(2019):1-16. |
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