一种基于花授粉算法的WRF风速集合预报新模型

A Novel Ensemble Forecasting Model for Wind Speed Based on WRF and FPA

基于不同水平分辨率和边界层参数化方案的集合预报思路,应用花授粉算法与不限制负值的约束理论(FPA-NNCT)进行权重平均,提出一种新的风速集合预报模型(FPA-NNCT-WRF-E).利用山东省代表山地和海滨下垫面的2个风电场风速实测数据,将新模型与传统算术集合模型(M-WRF-E)以及FPA模型(FPA-WRF-E)的风速预报结果进行对比评估.结果表明:FPA-NNCT-WRF-E预报明显优于M-WRF-E和FPA-WRF-E的风速预报,与M-WRF-E相比,FPA-WRF-E将风速平均绝对误差(MAE)减小了20%以上,而新模型FPA-NNCT-WRF-E将MAE减小了38%以上.预报的准确性得到了提高.

Considering multiple resolutions and boundary layer parameterizations, a novel ensemble forecast model (FPA-NNCT-WRF-E) is proposed in this paper, based on the weather research and forecasting model (WRF model), flower pollination algorithm (FPA) and no negative constraint theory (NNCT). And10-day wind observations from two wind farms of different underlying surface are used to validate the effectiveness of this new model. The results show that FPA-NNCT-WRF-E is superior to M- WRF-E and FPA-WRF-E. Compared with M-WRF-E, FPA-WRF-E has reduced the mean absolute error (MAE) by more than20%, while FPA-NNCT-WRF-E has reduced MAE by more than38%.