SHORT-TERM WIND SPEED FORECASTING BASED ON VARIABLE CORRELATION ATTENTION MECHANISM

被引：0
|
作者：
Wang X. [1 ]
Zhang K. [1 ]
Li X. [1 ]
Bai K. [1 ]
机构：
[1] Department of Automation, North China Electric Power University, Baoding
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 08期
关键词：
forecasting; maximal information coefficient; Transformer model; variable correlation attention mechanism; variational mode decomposition; wind power; wind speed;
D O I：
10.19912/j.0254-0096.tynxb.2022-0637
中图分类号：
学科分类号：
摘要：
A variable correlation attention mechanism-based short-term wind speed forecasting model is proposed for the multi-factor wind speed forecasting issue. The sequences strongly correlated with the wind speed sequence are screened out based on the sequence alignment and variable selection module，then the matrix made up of the samples of those sequences is generated. Meanwhile，a mode matrix is constructed from the VMD-decomposed historical wind speed sequence. The two matrixes are concatenated to generate the factor matrix. After that，the correlativity between the factor matrix and wind speed sequence is expressed by the variable correlation attention mechanism，and used to forecast the future wind speeds. Effectiveness of the sequence alignment and variable selection module，VMD module and variable correlation attention mechanism is validated on a one-year wind speed data measured from a wind power plant in southern China. The proposed model achieves the MAE values of 0.17，0.17，0.13 and 0.14 m/s in spring，summer，autumn and winter，respectively. Its performance is significantly better than that of the other comparative models，demonstrating the superiority of the proposed model. © 2023 Science Press. All rights reserved.
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页码：467 / 476
页数：9
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