Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition

作     者：Yingnan Zhao Guanlan Ji Fei Chen Peiyuan Ji Yi Cao 

作者机构：School of Computer and ScienceNanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer EngineeringUniversity of WindsorWindsorN9B 3P4Canada 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第43卷第11期

页      面：719-735页

核心收录：

学科分类：12[管理学] 02[经济学] 0202[经济学-应用经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 020204[经济学-金融学（含∶保险学）] 

基　　金：supported by the undergraduate training program for innovation and entrepreneurship of NUIST(XJDC202110300239) 

主　　题：Short-term wind speed prediction variational mode decomposition attention mechanism SENet BiLSTM 

摘      要：Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting *** paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind ***,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as ***,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal ***,VASTN accumulates the predictions of each IMF to obtain the predicted wind *** method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter *** results on real-world data demonstrate VASTN’s superiority over previous related algorithms.