基于CEEMDAN-AsyHyperBand-MultiTCN的短期风电功率预测

刘凡; 李捍东; 覃涛

doi:10.19912/j.0254-0096.tynxb.2022-1427

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (1) : 151-158. DOI: 10.19912/j.0254-0096.tynxb.2022-1427

基于CEEMDAN-AsyHyperBand-MultiTCN的短期风电功率预测

刘凡, 李捍东, 覃涛

作者信息 +

SHORT-TERM WIND POWER PREDICTION BASED ON CEEMDAN-AsyHyperBand-MultiTCN

Liu Fan, Li Handong, Qin Tao

Author information +

文章历史 +

摘要

为减少风电功率短期预测误差,提高风电利用效率,提出一种基于经验模态分解和异步超参数优化的多层时间卷积网络（CEEMDAN-AsyHyperBand-MultiTCN）的短期风电功率预测方法。首先,确定序列分量的数量,并使用自适应噪声完备集合经验模态分解（CEEMDAN）对原始风电功率进行分解,构成训练数据集。其次,使用深度残差级联（DRnet）构建多层的时间卷积网络（TCN）,并使用AsyHyperband算法对序列分量模型进行超参数寻优。最后,对序列分量分别进行预测,重构预测结果得到预测值。实验表明,该文提出的方法相比于其他方法能有效降低风电功率预测误差。

Abstract

In order to improve the utilization efficiency and accuracy of short-term wind power, this paper proposes a method based on complete ensemble empirical mode decomposition with adaptive noise and multi-layer temporal convolution networks （CEEMDAN-AsyHyperBand-MultiTCN）. Firstly, determine the number of sequence components, and then decompose the time series of wind power as training dataset using CEEMDAN. Secondly, apply the Deep Residual Cascade （DRnet） to build a multi-layer Temporal Convolutional Networks （TCN） model for each component, and the AsyHyperband algorithm is used to optimize the hyperparameters for the components model. Finally, the final prediction result is obtained after reconstructing the prediction results of each component model. The experimental results show that the proposed method can effectively reduce the wind power prediction error compared with other methods.

导出引用

刘凡, 李捍东, 覃涛. 基于CEEMDAN-AsyHyperBand-MultiTCN的短期风电功率预测[J]. 太阳能学报. 2024, 45(1): 151-158 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1427

Liu Fan, Li Handong, Qin Tao. SHORT-TERM WIND POWER PREDICTION BASED ON CEEMDAN-AsyHyperBand-MultiTCN[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 151-158 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1427

中图分类号： TM614

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