Ultra-short-term Photovoltaic Power Forecasting Based on Secondary Decomposition and BiGRU

HAN Bo; LI Changqing; LIU Weiliang; LIU Shuai; LIU Changliang; XU Jiahao; WANG Xin

doi:10.19805/j.cnki.jcspe.2025.230605

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Ultra-short-term Photovoltaic Power Forecasting Based on Secondary Decomposition and BiGRU

更新时间：2026-02-05

- Ultra-short-term Photovoltaic Power Forecasting Based on Secondary Decomposition and BiGRU
- Chinese Journal of Power Engineering Vol. 45, Issue 1, Pages: 62-69(2025)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院,河北,保定,071000
  2. 保定市综合能源系统状态检测与优化调控重点实验室,河北,保定,071000
  3. 国家能源集团新能源技术研究院有限公司,北京,102209
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.230605
  CLC： TM615
- Online First：15 January 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
韩博,李长青,刘卫亮,刘帅,刘长良,徐家豪,王昕. 基于二次分解和BiGRU的超短期光伏发电功率预测动力工程学报, 2025, 45(1): 62-69 https://doi.

org/10.19805/j.cnki.jcspe.2025.230605
韩博,李长青,刘卫亮,刘帅,刘长良,徐家豪,王昕. 基于二次分解和BiGRU的超短期光伏发电功率预测动力工程学报, 2025, 45(1): 62-69 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230605.

org/10.19805/j.cnki.jcspe.2025.230605 DOI：

摘要

针对超短期光伏发电功率预测

提出一种基于自适应噪声的完备经验模态分解(CEEMDAN)-变分模态分解(VMD)-双向门控循环单元(BiGRU)的混合预测模型。采用CEEMDAN对光伏发电功率信号进行分解

通过样本熵和K-means方法对分解后信号进行聚类重构;再利用VMD对复杂信号进行二次分解

以削弱信号的非平稳性;将分解后各信号分量作为BiGRU模型的输入进行训练、验证和预测

然后线性叠加各信号分量预测结果

得到最终预测结果。结果表明:混合预测模型的预测精度高于单一模型

验证了混合预测模型的有效性;通过对比典型天气情况下的预测效果及各项评价指标

验证了所提出方法的通用性。

Abstract

For the forecasting of ultra-short-term photovoltaic power

a hybrid prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)

variational mode decomposition (VMD) and bidirectional gated recurrent unit (BiGRU) was developed. The photovoltaic power generation signal was decomposed using CEEMDAN

and the decomposed signals were clustered and reconstructed using sample entropy and the K-means methods. Then

the VMD was applied for the secondary decomposition of complex signals to mitigate signal non-stationarity. The decomposed signal components were employed as inputs for training

validation and prediction in the BiGRU model. Subsequently

the predicted results from each signal component were linearly combined to obtain the final forecasting results. Results show that the hybrid model outperforms single models

confirming the effectiveness of the model. By comparing the forecasting performances under typical weather conditions and various evaluation metrics

the generality of the proposed method was validated.

关键词

Keywords

references

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