基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测

李天白; 顾军华; 秦玉龙; 张素琪

doi:10.19912/j.0254-0096.tynxb.2024-1607

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (1) : 604-612. DOI: 10.19912/j.0254-0096.tynxb.2024-1607

基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测

李天白¹, 顾军华^1,2, 秦玉龙¹, 张素琪³

作者信息 +

ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON INFORMATION ENTROPY CLUSTERING DECOMPOSITION AND CTA-BILSTM

Li Tianbai¹, Gu Junhua^1,2, Qin Yulong¹, Zhang Suqi³

Author information +

文章历史 +

摘要

针对风电功率序列非平稳性和波动性的问题,提出一种超短期风电功率预测框架,该框架由两部分组成：信息熵聚类分解和通道时序注意力双向长短期记忆网络预测模型。首先,对风电功率序列进行信息熵聚类分解,过程为应用改进完全集合经验模态分解对风电功率进行一次分解,将分解后得到的高复杂度模态分量使用变分模态分解进行二次分解,根据信息熵将相似性高的分量聚类形成新的聚类模态分量;然后,将各分量输入通道时序注意力双向长短期记忆网络预测模型中进行预测;最后,使用中国西北地区某风电场的数据集进行实验。实验结果显示该文所提框架与现有优秀风电功率预测模型框架相比具有更高的预测精度。

Abstract

To address the non-stationarity and volatility of wind power series, this paper proposes a short-term wind power forecasting framework consisting of two parts： information entropy clustering decomposition and a channel-time attention bidirectional long short-term memory network forecasting model. Firstly, the wind power series undergoes information entropy clustering decomposition. The improved complete ensemble empirical mode decomposition with adaptive noise is utilized for the initial decomposition, and the high-complexity components derived from this process are subsequently decomposed using variational mode decomposition. Based on information entropy, components with high similarity are clustered to form new modal components.Secondly, these decomposed components are fed into the CTA-BiLSTM forecasting model. This model employs both channel attention and temporal attention mechanisms to assign different weights to features based on their importance. Finally, experiments are conducted using a dataset from a wind farm in northwest China. The experimental results show that the proposed framework attains superior forecasting accuracy in comparison to current state-of-the-art models.

导出引用

李天白, 顾军华, 秦玉龙, 张素琪. 基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测[J]. 太阳能学报. 2026, 47(1): 604-612 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1607

Li Tianbai, Gu Junhua, Qin Yulong, Zhang Suqi. ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON INFORMATION ENTROPY CLUSTERING DECOMPOSITION AND CTA-BILSTM[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 604-612 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1607

中图分类号： TM614

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