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

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (12) : 333-342. DOI: 10.19912/j.0254-0096.tynxb.2024-1144

基于OVMD-CNN-BiLSTM的短期光伏发电预测

梁富源¹, 万艳妮², 杨国华^2,3, 李方圆⁴, 张梦晴², 杜文超²

作者信息 +

SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTIONBASED ON OVMD-CNN-BiLSTM

Liang Fuyuan¹, Wan Yanni², Yang Guohua^2,3, Li Fangyuan⁴, Zhang Mengqing², Du Wenchao²

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文章历史 +

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.

导出引用

梁富源, 万艳妮, 杨国华, 李方圆, 张梦晴, 杜文超. 基于OVMD-CNN-BiLSTM的短期光伏发电预测[J]. 太阳能学报. 2025, 46(12): 333-342 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1144

Liang Fuyuan, Wan Yanni, Yang Guohua, Li Fangyuan, Zhang Mengqing, Du Wenchao. SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTIONBASED ON OVMD-CNN-BiLSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 333-342 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1144

中图分类号： TM615

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