SHORT-TERM WIND POWER PREDICTION BASED ON TEMPORAL CONVOLUTIONAL NETWORK RESIDUAL CORRECTION MODEL

被引：0
|
作者：
Su L. [1 ]
Zhu J. [1 ]
Li Y. [2 ]
机构：
[1] School of Electrical Engineering, Yanshan University, Qinhuangdao
[2] School of Information Science and Engineering, Yanshan University, Qinhuangdao
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 07期
关键词：
complete ensemble empirical mode decomposition with adaptive noise; grey relational analysis; residual correction; temporal convolutional network; wind power prediction;
D O I：
10.19912/j.0254-0096.tynxb.2022-0380
中图分类号：
学科分类号：
摘要：
A short-term wind power prediction method based on temporal convolutional network residual correction model is proposed to improve the accuracy of short-term wind power prediction. Firstly，using the complete ensemble empirical mode decomposition with adaptive noise algorithm to separate the local characteristic information of original wind power data，each component is predicted by the support vector regression model which is optimized by grid search and cross-validation algorithm. Secondly，a temporal convolutional network residual prediction model is constructed，and the gray correlation analysis method is used to select the input features of the residual prediction model to correct the support vector regression prediction results. Finally，based on the proposed model，the actual operating power of a wind farm is predicted and compared with the prediction accuracy of other methods. The results verify that the proposed method improves the accuracy of short-term wind power prediction. © 2023 Science Press. All rights reserved.
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页码：427 / 435
页数：8
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