RESEARCH ON ULTRA-SHORT-TERM WIND POWER FORECAST BASED ON AVMD-CNN-GRU-Attention

被引：0
|
作者：
Ren D. [1 ]
Ma J. [1 ]
He Z. [1 ]
Wu Q. [1 ]
机构：
[1] Electrical Engineering College, Guizhou University, Guiyang
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 06期
关键词：
attention mechanism; convolutional neural network; forecasting; sample entropy; variational mode decomposition; wind power;
D O I：
10.19912/j.0254-0096.tynxb.2023-0146
中图分类号：
学科分类号：
摘要：
In order to improve the forecast accuracy of ultra-short-term wind power，an improved ultra-short-term wind power forecast model based on variational mode decomposition convolutional neural network（AVMD-CNN），gated recurrent unit（GRU）and attention mechanism（Attention）is proposed. Firstly，the wind power sequence is decomposed into K sub-modes by using the improved VMD. Then，each sub-mode is classified by sample entropy（SE）and center frequency. According to the classification results，each sub-mode is given a normalization method，and input into GRU-Attention and CNN-GRU-Attention models for training and forecasting according to SE values. Finally，the final results are obtained by superimposing the forecast results of each sub-mode，so as to complete the ultra-short-term wind power forecast. Using the determination coefficient （R2），mean absolute error（MAE），root mean square error（RMSE），and mean absolute percentage error（MAPE）as the accuracy assessment indexes，the actual arithmetic examples show that the R2 of the proposed model is improved by 12.06% on average compared with other methods，and the MAE，RMSE，and MAPE are reduced by 59.36%，62.49%，and 48.34% respectively，with high prediction accuracy. © 2024 Science Press. All rights reserved.
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页码：436 / 443
页数：7
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