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/m2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/m2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/m2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。
Abstract
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/m2 , 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/m2 and 700 W/m2 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/m2. 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.
关键词
太阳辐射 /
预报 /
人工智能 /
多模式集成 /
光伏电站
Key words
solar radiation /
forecasting /
artificial intelligence /
multi-model ensemble /
PV station
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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）
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