Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Power Stations Based on Dynamic Variable Weight  

作　　者：YUAN Bin SHEN Yan-bo DENG Hua YANG Yang CHEN Qi-ying YE Dong MO Jing-yue YAO Jin-feng LIU Zong-hui 袁彬;申彦波;邓华;杨扬;陈起英;叶冬;莫景越;姚锦烽;刘宗会(Public Meteorological Service Center,China Meteorological Administration,Beijing 100081 China;Key Laboratory of Energy Meteorology,China Meteorological Administration,Beijing 100081 China;Guangzhou Institute of Tropical and Marine Meteorology,China Meteorological Administration,Guangzhou 510641 China;Institute of Urban Meteorology,China Meteorological Administration,Beijing 100089 China;Center for Earth System Modeling and Prediction,China Meteorological Administration,Beijing 100081 China;Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002 China)

机构地区：[1]Public Meteorological Service Center,China Meteorological Administration,Beijing 100081 China [2]Key Laboratory of Energy Meteorology,China Meteorological Administration,Beijing 100081 China [3]Guangzhou Institute of Tropical and Marine Meteorology,China Meteorological Administration,Guangzhou 510641 China [4]Institute of Urban Meteorology,China Meteorological Administration,Beijing 100089 China [5]Center for Earth System Modeling and Prediction,China Meteorological Administration,Beijing 100081 China [6]Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002 China

出　　处：《Journal of Tropical Meteorology》2024年第3期327-336,共10页热带气象学报（英文版）

基　　金：Innovation and Development Project of China Meteorological Administration(CXFZ2023J044);Innovation Foundation of CMA Public Meteorological Service Center(K2023002);“Tianchi Talents”Introduction Plan(2023);Key Innovation Team for Energy and Meteorology of China Meteorological Administration。

摘　　要：In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.

关 键 词：GHI forecast multimodel ensemble dynamic variable weight PV power station 

分 类 号：P456.1[天文地球—大气科学及气象学]