Short-term Photovoltaic Power Prediction Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-sample Entropy-bidirectional Long Short-term Memory

PAN Ruokuan; ZHU Xiaojing

doi:10.19725/j.cnki.1007-2322.2023.0423

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Short-term Photovoltaic Power Prediction Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-sample Entropy-bidirectional Long Short-term Memory

更新时间：2026-04-02

- Short-term Photovoltaic Power Prediction Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-sample Entropy-bidirectional Long Short-term Memory
- Vol. 43, Issue 2, Pages: 235-243(2026)
- 作者机构：
  
  上海电力大学数理学院, 上海市浦东新区,201306
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0423
  CLC： 2
- Published：2026
- 稿件说明：
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PAN Ruokuan, ZHU Xiaojing. Short-term Photovoltaic Power Prediction Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-sample Entropy-bidirectional Long Short-term Memory[J]. 2026, 43(2): 235-243.
DOI：

PAN Ruokuan, ZHU Xiaojing. Short-term Photovoltaic Power Prediction Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-sample Entropy-bidirectional Long Short-term Memory[J]. 2026, 43(2): 235-243. DOI： 10.19725/j.cnki.1007-2322.2023.0423.

摘要

光伏发电具有间歇性和波动性，传统的单一模型难以实现精确预测。因此，提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise

CEEMDAN)、样本熵(sample entropy

SE)和双向长短期记忆网络(bidirectional long short-term memory

Bi-LSTM)组合的预测模型。首先，采用CEEMDAN对历史功率序列进行分解以降低其非平稳性，针对分解后导致后续预测数据规模增大的问题，通过引入样本熵的方法将子序列进行重组。其次，将重组序列输入到双向长短期记忆网络中，对其进行学习和预测。最后，通过将各个重组序列预测结果线性相加的方式，获得最终的预测结果。实例验证表明，构建的组合模型适用于不同天气条件下的光伏功率预测，并且具有更高的预测精度。

Abstract

Photovoltaic power generation exhibits the characteristics of intermittence and great fluctuation

posing challenges for the traditional single model to achieve accurate prediction. Therefore

a prediction model is proposed based on a combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)

sample entropy (SE) and bi-directional long-short-term memory (Bi-LSTM). Firstly

the historical power sequences are decomposed using CEEMDAN to mitigate its non-stationarity. The subsequent sequences are reorganized by incorporating sample entropy to address the issue of increased data size in subsequent prediction after the decomposition. Secondly

the reorganized sequences are fed into a Bi-LSTM network for training and prediction. Finally

the final prediction results are obtained by linearly summing up the prediction results of each reorganized sequence. The case validation demonstrates that the constructed combined model is suitable for PV power prediction under diverse weather conditions and exhibits higher prediction accuracy.

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Keywords

references

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