基于机器学习的风电场风速多模式集合预报

Wind Speed Multi-Mode Ensemble Forecasting for Wind Farms Based on Machine Learning

[目的]随着大量风电场的兴建,组合研究不同的机器学习算法和气象预报模式已成为研究焦点。[方法]文章以湖北省风能资源的空间分布特征为基础,通过选取代表站点结合实验数据分析对结果进行深入探讨。[结果]在湖北省,已建和在建的风电场主要集中在“三带一区”的区域,具体包括:位于湖北省中部,从荆门至荆州的南北向风带;位于鄂北,从枣阳至英山的东西向风带;部分湖岛和沿湖地带;以及鄂西南和鄂东南的部分高山地区。该研究采用4种不同的数值预报产品,包括CMA-WSP、CMA-GD、WHMM和EC,与实测风速对比深入探究这些数值模式的适用范围。[结论]通过分析基于机器学习的5种集合预报方法及均值法在湖北省各地区的表现确定了适合的算法和预报模式组合,为提高集合预报的准确性提供了参考。

[Introduction]With the extensive construction of wind farms,the combination of researches on different machine learning algorithms and meteorological forecasting modes has received widespread attention.[Method]This paper was based on the spatial distribution characteristics of wind energy resources in Hubei Province,and utilized representative stations in combination with experimental data analysis to conduct in-depth discussions on the results.[Result]The wind farms in operation and under construction in Hubei Province are all located in the"Three Zones and One Area",including the north-south wind zone from Jingmen to Jingzhou in the central part of Hubei Province,the east-west wind zone from Zaoyang to Yingshan in the north of Hubei Province,certain lake islands and zones along the lake,as well as some high mountainous areas in the southwest and southeast of Hubei Province.This research uses four different numerical forecasting products,namely CMA-WSP,CMA-GD,WHMM,and EC,to compare with the measured wind speeds and investigated the applicable range of these four numerical modes.[Conclusion]By analyzing the performance of five ensemble forecasting methods based on machine learning and the mean method,we identified suitable algorithm and forecasting model combinations,providing references for improving the accuracy of ensemble forecasting.