基于SVMD-Informer-XGBoost的风电功率区间预测  

Wind Power Power Interval Prediction Based on SvMD-Informer-XGBoost

作　　者：黄丹[1] 张历卓 陈思羽[1] 匡迎春[1] HUANG Dan;ZHANG Lizhuo;CHEN Siyu;KUANG Yingchun(College of Infomation and Intelligent science and Technology,Hunan Agricultural University,Changsha Hunan 410128)

机构地区：[1]湖南农业大学信息与智能科学技术学院,湖南长沙410128

出　　处：《长江信息通信》2024年第10期34-37,共4页Changjiang Information & Communications

摘　　要：为了提高风电功率区间预测的精度,文章提出逐次变分模态分解(SVMD)、Informer和XGBoost算法模型的结合,实现风电功率区间预测。首先,SVMD算法自适应地分解出风电功率数据的各模态分量(IMF)和残差分量。其次,通过网格搜索算法(CV)对Informer和XGBoost模型算法进行优化,并通过网格搜索算法确定Informer-XGBoost输出预测值的最优权重,构建基于SVMD-Informer-XGBoost的预测模型。最后,通过分位数回归法(Quantile regression)输出预测值和真实值的误差,得到风电功率的区间预测。实验结果表明,所提出的方法相较于传统单一模型,在区间预测的准确度和稳定性上有了显著提升。在95%置信度水平下,区间预测指标区间覆盖率(PICP)和区间平均宽度(PINAW)分别可达94.96%和0.0362。In order to improve the accuracy of wind power interval prediction,this paper propo-ses a combination of successive variational mode decomposition(SVMD),Informer and XG-Boost algorithm models to achieve wind power interval prediction.First,the SVMD algorithm adaptively dccomposcs cach modal componcnt(IMF)and residual componcnt of wind power da-ta.Secondly,the Informer and XGBoost model algorithms are optimized through the grid search algorithm(CV),and the optimal weight of the Informer-XGBoost output prediction value is de-termined through the grid search algorithm to build a prediction model based on SVMD-In-former-XGBoost.Finally,the quantile regression method is used to output the crror betwecn the predicted value and the true value to obtain the interval prediction of wind power.Experimental results show that thc proposed mcthod has significantly improved the accuracy and stability of interval prediction compared with the traditional single model.At the 95%confidence level,the interval prediction index interval coverage(PICP)and interval average width(PINA W)can reach 94.96%and 0.0362 respectively.

关 键 词：逐次变分模态分解 XGBoost INFORMER 网格搜索 

分 类 号：TM72[电气工程—电力系统及自动化]