摘要
基于2022年CMA-WSP、CMA-MESO、CMA-GD、WRF-SOLAR 4个数值模式预报以及广东省阳江市4个光伏电站实况观测数据,采用LightGBM集成模型,开展逐月总辐射辐照度(GHI)多模式集成预报试验。结果表明:多模式集成可有效降低GHI预报的平均绝对误差(MAE)和均方根误差(RMSE),与每月的最优数值模式预报相比,MAE减少2.47%~32.71%、RMSE减少5.46%~32.29%;多模式集成在不同GHI区间效果差异明显,400 W/m^(2)以下区间内,多模式集成效果最好,全年12个月中有10个月集成有效,MAE减少6.25%~44.44%、RMSE减少14.62%~43.07%,400~700 W/m^(2)区间内多模式集成效果次之,全年12个月中有6个月集成有效,MAE减少0.76%~34.59%、RMSE减少4.14%~31.11%,大于700 W/m^(2)区间内受限于样本量,多模式集成无效果;在晴天、少云、多云、阴天4种典型天气条件下,多模式集成预报与实况观测趋势最为接近,且能体现出因云量变化造成的GHI波动。
Based on the forecast of 4 numerical models:CMA-WSP,CMA-MESO,CMA-GD and WRF-SOLAR,as well as the observation data of 2022 collecting from 4 PV stations in Yangjiang city,Guangdong Province,the monthly multi-model ensemble forecasting experiments of global horizontal irradiance(GHI)were conducted by using LightGBM ensemble model.The results show that multi-model ensemble,when compared with the monthly optimal numerical model,can effectively reduce MAE and RMSE of GHI forecast by a range of 2.47%-32.71%and 5.46%-32.29%,respectively.There are considerable differences in the results of multi-model ensemble at different GHI value intervals.When GHI is below 400 W/m^(2),the multi-model ensemble achieves the best performance,with 6.25%-44.44%decrease of MAE and 14.62%-43.07%decrease of RMSE,for 10 months in the entire year.The effect of the ensemble in the GHI interval between 400 W/m^(2) and 700 W/m^(2) ranks second,and the decreasing ranges of MAE and RMSE,for 6 months in the entire year,are 0.76%-34.59%and 4.14%-31.11%respectively.The multi-model ensemble has no effect,due to insufficient sample,when GHI is greater than 700 W/m^(2).Under 4 typical weather conditions,i.e clear,partly cloudy,cloudy and overcast,the multi-model ensemble forecast is the closest to the real observation trend,and it also can illustrate the radiation fluctuation caused by variation of cloud cover.
作者
袁彬
于廷照
申彦波
莫景越
邓华
Yuan Bin;Yu Tingzhao;Shen Yanbo;Mo Jingyue;Deng Hua(CMA Public Meteorological Service Centre,Beijing 100081,China;CMA Wind and Solar Energy Centre,Beijing 100081,China;Guangzhou Institute of Tropical and Marine Meteorology,China Meteorological Administration,Guangzhou 510641,China)
出处
《太阳能学报》
北大核心
2025年第4期291-300,共10页
Acta Energiae Solaris Sinica
基金
中国气象局创新发展专项(CXFZ2024J068)
中国气象局公共气象服务中心创新基金(K2023002)
新疆“天池英才”引进计划(2023)。
关键词
太阳辐射
预报
人工智能
多模式集成
光伏电站
solar radiation
forecasting
artificial intelligence
multi-model ensemble
PV station
