Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

作     者：MOU Xingyu CHEN Hui ZHANG Xinjing XU Xin YU Qingbo LI Yunfeng MOU Xingyu;CHEN Hui;ZHANG Xinjing;XU Xin;YU Qingbo;LI Yunfeng

作者机构：Jilin Province Meteorological Information Network CenterChangchun 130062JilinChina 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2023年第28卷第2期

页      面：169-176页

核心收录：

学科分类：08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Science and Technology Basic Work Project of China Meteorological Administration(2005DKA31700-06) Innovation Fund of Public Meteorological Service Center of China Meteorological Administration(M2020013) 

主　　题：meteorological features short-term power load forecasting Cat Boost wind power 

摘      要：As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction ***,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is ***,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is ***,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single ***,a prediction method based on Cat Boost network is constructed to further realize short-term wind power *** National Renewable Energy Laboratory(NREL)dataset is used for experimental *** results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.