Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

doi:10.3390/atmos11070738

	Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm
	Xu, Wenqing 1; Ning, Like 2,3; Luo, Yong 1
刊名	ATMOSPHERE
	2020-07-01
卷号	11 期号:7 页码:38
关键词	wind speed forecast numerical weather prediction post processing gradient boosting decision tree
DOI	10.3390/atmos11070738
通讯作者	Luo, Yong(yongluo@tsinghua.edu.cn)
英文摘要	With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7-3.5 m/s in the original WRF result by 1-1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10-0.20, correlation coefficient be improved by 0.10-0.18, Nash-Sutcliffe efficiency coefficient (NSE) be improved by -0.06-0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.
资助项目	National Key Research and Development Program of China[2018YFB1502803] ; Scientific Research Program of Tsinghua University Research on Wind Farm Weather Forecasting Technology for Power Grid
WOS关键词	3DVAR DATA ASSIMILATION ; ACCURACY ENHANCEMENT ; SURFACE-TEMPERATURE ; MESOSCALE-MODEL ; ENSEMBLE ; SYSTEM ; MOS ; STATISTICS ; PARAMETERS ; SCHEMES
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	MDPI
WOS记录号	WOS:000572570500001
资助机构	National Key Research and Development Program of China ; Scientific Research Program of Tsinghua University Research on Wind Farm Weather Forecasting Technology for Power Grid
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156943]
专题	中国科学院地理科学与资源研究所
通讯作者	Luo, Yong
作者单位	1.Tsinghua Univ, Dept Earth Syst Sci, Minist Educ Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Yucheng Comprehens Expt Stn, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Xu, Wenqing,Ning, Like,Luo, Yong. Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm[J]. ATMOSPHERE,2020,11(7):38.
APA	Xu, Wenqing,Ning, Like,&Luo, Yong.(2020).Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm.ATMOSPHERE,11(7),38.
MLA	Xu, Wenqing,et al."Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm".ATMOSPHERE 11.7(2020):38.