基于OVMD-CNN-BiLSTM的短期光伏发电预测

梁富源; 万艳妮; 杨国华; 李方圆; 张梦晴; 杜文超

doi:doi:10.19912/j.0254-0096.tynxb.2024-1144

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基于OVMD-CNN-BiLSTM的短期光伏发电预测

更新时间：2026-01-08

- 基于OVMD-CNN-BiLSTM的短期光伏发电预测
- Vol. 46, Issue 12, Pages: 333-342(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1144
  CLC： TM615
- Published Online：07 January 2026，
  
  Published：2025
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梁富源, 万艳妮, 杨国华, et al. 基于OVMD-CNN-BiLSTM的短期光伏发电预测[J]. 2025, 46(12): 333-342.
DOI：

梁富源, 万艳妮, 杨国华, et al. 基于OVMD-CNN-BiLSTM的短期光伏发电预测[J]. 2025, 46(12): 333-342. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1144.

摘要

提出一种基于灰狼优化算法（GWO）改进变分模态分解（VMD）的卷积神经网络-双向长短期记忆网络（CNN-BiLSTM）预测模型以提高短期光伏发电功率的预测精度。首先

将预处理后的光伏发电数据通过最优变分模态分解（OVMD）算法分解为多个不同频率的模态分量;然后

将各模态信号及相关影响因素作为CNN-BiLSTM网络的输入进行训练、验证和测试;最后

将各模态分量的预测结果重构相加并分析预测误差。通过宁夏某光伏电站实测数据验证表明

该文提出的OVMD-CNN-BiLSTM模型相较于多种基准模型在预测精度和稳定性方面具有显著优势。

Abstract

As a kind of clean and environmentally friendly sustainable energy

photovoltaic power generation plays an important role in the process of building a new type of power system with new energy as the main body. However

the random intermittency of photovoltaic power generation brings great challenges to the stable operation of the grid. Therefore

this paper proposes convolutional neural network-bidirectional long short-term memory （CNN-BiLSTM） prediction model based on grey wolf optimization （GWO） algorithm improved variational mode decomposition （VMD） to improve the accuracy of short-term photovoltaic power generation prediction. Firstly

the pre-processed PV power generation data is decomposed into multiple frequency modal components by optimal variational mode decomposition （OVMD） algorithm. Then

different modal signals and related influencing factors are used as the input of CNN-BiLSTM network for training

verification and testing. Finally

the prediction error is analyzed and reconstructed. The simulation results based on the practical data of a photovoltaic power station in Ningxia verifies that the OVMD-CNN-BiLSTM model proposed in this paper has significant advantages in prediction accuracy and stability compared with various reference models.

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