基于机器学习的100m高度风速短期预报订正方法  

SHORT-TERM 100-METERS WIND SPEED FORECAST CORRECTION METHOD BASED ON MACHINE LEARNING

作　　者：高金兵 曹润东 于廷照 姚锦烽[1,2] 申彦波[1,2,3] Gao Jinbing;Cao Rundong;Yu Tingzhao;Yao Jinfeng;Shen Yanbo(Public Meteorological Service Center of China Meteorological Administration,Beijing 100081,China;Wind and Solar Energy Centre of China Meteorological Administration,Beijing 100081,China;Key Laboratory of Energy Meteorology,China Meteorological Administration,Beijing 100081,China)

机构地区：[1]中国气象局公共气象服务中心,北京100081 [2]中国气象局风能太阳能中心,北京100081 [3]中国气象局能源气象重点开放实验室,北京100081

出　　处：《太阳能学报》2025年第1期71-77,共7页Acta Energiae Solaris Sinica

基　　金：中国气象局公共气象服务中心创新基金(K2022002);西藏自治区科技重大专项(XZ202201ZD0003G05);国家自然科学基金(62106270)。

摘　　要：提出一种基于机器学习的先格点订正区域误差、后站点订正局地误差的100 m高度风速短期预报订正方法。首先,基于100 m高度风速格点实况资料,采用深度学习算法对数值天气预报进行格点订正;然后,基于测风塔100 m高度风速观测资料,采用随机森林集成预报订正方法对站点风速预报进行订正;最后,选用内蒙古中部某站点进行分析验证。结果表明,订正前未来84小时风速预报均方根误差(RMSE)为4.25 m/s,格点订正后RMSE降至3.16~3.79 m/s,站点集成订正后RMSE降至2.81 m/s。格点订正后预报误差明显降低,站点集成订正后误差进一步减小。In this paper,a forecast correction method based on machine learning is proposed,which first corrects regional errors with gridded analysis products and then corrects local errors with station observation data.The method utilizes the gridded wind speed analysis products at 100-meter height to correct the results of numerical weather prediction through deep learning algorithms,and then corrects the station wind speed forecast results with a random forest integrated forecast approach based on the observation data of wind speed at 100-meter height from the anemometer tower.An empirical analysis of a meteorological station in central Inner Mongolia shows that after correction with the proposed method,the root mean square error(RMSE)of wind speed prediction for the next 84 hours is significantly reduced from 4.25 m/s to 3.16-3.79 m/s after gridded forecast correction and to 2.81 m/s after station integrated correction.Therefore,the proposed method can effectively reduce forecast errors.

关 键 词：100m高度风速预报 预报订正 机器学习 深度学习 集成预报 

分 类 号：TM614[电气工程—电力系统及自动化]