基于样本熵双分解和SSA-LSTM的超短期风速预测

王希; 孙娜; 苏浩; 张楠; 张帅; 纪捷

doi:10.19912/j.0254-0096.tynxb.2023-1888

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基于样本熵双分解和SSA-LSTM的超短期风速预测

更新时间：2025-10-18

- 基于样本熵双分解和SSA-LSTM的超短期风速预测
- ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON SAMPLE ENTROPY-BASED DUAL DECOMPOSITION AND SSA-LSTM
- 太阳能学报 2025年46卷第3期页码：611-618
- 作者机构：
  
  1. 淮阴工学院自动化学院,淮安 223003
  2. 西安交通大学能源与动力工程学院热流科学与工程教育部重点实验室,西安 710049
- 作者简介：
- 基金信息：
  
  江苏省高校自然科学基金（20KJD480003）;江苏省双创计划（JSSCBS（2020）31035）;江苏省自然科学基金（BK20201069）
- DOI：10.19912/j.0254-0096.tynxb.2023-1888
  中图分类号： TM614
- 网络出版：2025-04-07，
  
  纸质出版：2025
- 稿件说明：
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王希, 孙娜, 苏浩, 张楠, 张帅, 纪捷. 基于样本熵双分解和SSA-LSTM的超短期风速预测[J]. 太阳能学报, 2025, 46(3): 611-618.

Wang Xi, Sun Na, Su Hao, et al. ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON SAMPLE ENTROPY-BASED DUAL DECOMPOSITION AND SSA-LSTM[J]. 2025, 46(3): 611-618.
王希, 孙娜, 苏浩, 张楠, 张帅, 纪捷. 基于样本熵双分解和SSA-LSTM的超短期风速预测[J]. 太阳能学报, 2025, 46(3): 611-618. DOI： 10.19912/j.0254-0096.tynxb.2023-1888.

Wang Xi, Sun Na, Su Hao, et al. ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON SAMPLE ENTROPY-BASED DUAL DECOMPOSITION AND SSA-LSTM[J]. 2025, 46(3): 611-618. DOI： 10.19912/j.0254-0096.tynxb.2023-1888.

摘要

为进一步提高超短期风速预测的准确性

提出一种基于样本熵的双分解和麻雀搜索算法（SSA）改进长短时记忆神经网络（ILSTM）的耦合模型（简记为DILSTM）。首先

利用变分模态分解（VMD）分解原始序列并通过样本熵量化各子序列的复杂性;其次

利用完全自适应噪声集合经验模态分解（CEEMDAN）分解复杂度最高的子序列进一步提取特征过滤噪声;最后

将双分解得到的子序列分别建立DILSTM预测模型

并对所有子序列的预测结果叠加得到最终风速预测结果。真实风场数据实验结果表明

所提模型与LSTM、随机森林（RF）、卷积神经网络（CNN）单一模型相比

提升约25%

RMSE降低约65%;同时

与现有同类研究对比

也证实了所提DILSTM模型的优势

该文为提高超短期风速预测精准度提供了一种新的方法。

Abstract

To improve the accuracy of ultra-short-term wind speed prediction

a coupled model named DILSTM is proposed

which combines sample entropy

dual decomposition

and sparrow search algorithm optimized long short-term memory neural network （LSTM）. Firstly

the original sequence is decomposed using variational mode decomposition （VMD） and the complexity of each sub sequence is quantified through sample entropy; the subsequence with the highest complexity is further decomposed using complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）

further extracting features and filtering noise; Finally

the sub sequences obtained from the double decomposition are used to establish DILSTM prediction models

and

the final wind speed prediction results are obtained by overlaying the prediction results of all sub sequences. The experimental results of real wind field data show the proposed model outperforms single models such as LSTM

random forest （RF）

and convolutional neural network （CNN）

with an approximately 25% increase in R

and a 65% decrease in RMSE; Meanwhile

compared with existing similar studies

the advantages of the DILSTM model proposed in this paper have been confirmed. This study provides a new method for improving the accuracy of ultra-short-term wind speed prediction.

关键词

Keywords

references

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