In response to the problem of poor prediction accuracy,ignoring the chaotic characteristics of wind speed and the implicit correlation within modal components of traditional models.This article proposes an ultra short term wind speed prediction model that combines modal decomposition,feature selection,and Informer.Firstly,an improved fully adaptive noise set empirical mode decomposition is used to decompose the original wind speed sequence into modal components.At the same time,the chaotic wind speed original sequence is reconstructed in phase space to obtain the wind chaotic speed sequence.Subsequently,a fast correlation based filtering algorithm is used to filter the features of modal components and chaotic sequences,achieving optimal selection of modal components.Finally,the selected modal components are input into the Informer model to output future wind speeds.The accuracy and effectiveness of the prediction model were verified through simulation calculations,by using the wind field in the Northeast region as the research object.
关键词
集成学习/多元特征提取/Informer模型
Key words
integrated learning/multivariate feature extraction/Informer model
维普
万方数据