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

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基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测

更新时间：2026-02-06

- 基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测
- 基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测
- 太阳能学报 2026年47卷第1期页码：604-612
- 作者机构：
- 作者简介：
- 基金信息：
  
  天津市自然科学基金（22JCQNJC01450）;省部共建电工装备可靠性与智能化国家重点实验室（河北工业大学）优秀青年创新基金（EERI_OY2022005）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1607
  中图分类号： TM614
- 网络首发：2026-02-05，
  
  纸质出版：2026-02-05
- 稿件说明：
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李天白, 顾军华, 秦玉龙, 等. 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.

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

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

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.

关键词

Keywords

references

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