[1]林志辉,刘丽桑,张立伟,等.基于VMD-TCN-BIGRU的超短期功率预测[J].福建理工大学学报,2025,23(04):374-381.[doi:10.3969/j.issn.2097-3853.2025.04.010]
　LIN Zhihui,LIU Lisang,ZHANG Liwei,et al.Ultra-short-term power prediction based on VMD-TCN-BIGRU[J].Journal of Fujian University of Technology;,2025,23(04):374-381.[doi:10.3969/j.issn.2097-3853.2025.04.010]
点击复制

基于VMD-TCN-BIGRU的超短期功率预测()
分享到：

《福建理工大学学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第23卷
期数:
2025年04期
页码:
374-381
栏目:
出版日期:
2025-08-25

文章信息/Info

Title:
Ultra-short-term power prediction based on VMD-TCN-BIGRU
作者:
林志辉; 刘丽桑; 张立伟; 陈文伟
福建理工大学电子电气与物理学院
Author(s):
LIN Zhihui;  LIU Lisang;  ZHANG Liwei;  CHEN Wenwei
School of Electronic, Electrical and Physics, Fujian University of Technology
关键词:
灰狼优化算法; 变分模态分解; 时序卷积网络; 双向门循环单元
Keywords:
grey wolf optimizer(GWO);  variational mode decomposition (VMD);  temporal convolutional network(TCN);  bidirectional gated recurrent unit(BGRU)
分类号:
TM715
DOI:
10.3969/j.issn.2097-3853.2025.04.010
文献标志码:
A
摘要:
为提高电力系统超短期功率预测的准确性,提出了一种基于变分模态分解(VMD) 和时序卷积网络-双向门控循环单元(TCN-BIGRU)的预测模型。通过灰狼优化算法(GWO)优化VMD 的模态数量和惩罚因子,以提升信号分解质量并减少噪声干扰。将分解后的模态与基于皮尔逊相关系数提取的特征向量输入TCN-BIGRU 模型,提取时间序列的短期与长期依赖特征,从而提升预测准确性。实验结果显示,该模型的预测精度达99.144%,优于传统单一模型及其他组合模型,在应对非线性与波动性数据上适应性更强、预测误差更低。
Abstract:
To improve the accuracy of ultra-short-term power prediction in power systems, a prediction model is proposed based on variational mode decomposition (VMD) and temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU). The grey wolf optimizer (GWO) is used to optimize the number of modes and the penalty factor of VMD, aiming to enhance the quality of signal decomposition and reduce noise interference. The decomposed modes and the feature vectors extracted based on the Pearson correlation coefficient are input into the TCN-BiGRU model to extract short-term and long-term dependence features of time series, thereby improving the prediction accuracy. Experimental results show that the prediction accuracy of this model reaches 99.144%, which is superior to that of traditional single models and other combined models. It has stronger adaptability in dealing with nonlinear and volatile data and has lower prediction error.

参考文献/References:

［1］ 谢丽蓉,王斌,包洪印,等. 基于EEMD-WOA-LSSVM的超短期风电功率预测［J］. 太阳能学报,2021,42(7):290-296.［2］ KHALID M, SAVKIN A V. A method for short-term wind power prediction with multiple observation points［J］. IEEE Transactions on Power Systems, 2012, 27(2): 579-586.［3］ WANG K J，QI X X，LIU H D. Photovoltaic power forecasting based LSTMconvolutional network［J］. Energy，2019，189：116225.［4］ 杨祎玥，伏潜，万定生. 基于深度循环神经网络的时间序列预测模型［J］. 计算机技术与发展，2017，27（3）：35-38，43.［5］ 查雯婷，杨帆，陈波，等. 基于CNN的区域风功率预测方法［J］. 计算机仿真，2021，38（5）：318-323.［6］ CHEN P, LI J. Wind power prediction based on NGO-LSTM［C］∥Journal of Physics: Conference Series. IOP Publishing, 2024, 2782(1): 012068.［7］ 赵全明，李珂，王笑欢，等. 基于SSAGRU神经网络的超短期风电功率预测［J］. 传感器与微系统，2023，42（11）：151-155.［8］ NIE Y，JIANG P，ZHANG H P. A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting［J］. Applied Soft Computing，2020，97：106809.［9］ 王佳钰，郝思鹏，李森文，等. 基于ES-GRU-LSTM的风电场群功率预测［J］. 计算技术与自动化，2022，41（3）：37-41.［10］ 孔祥玉，李闯，郑锋，等. 基于经验模态分解与特征相关分析的短期负荷预测方法［J］. 电力系统自动化，2019，43（5）：46-52.［11］ 郭玲，徐青山，郑乐. 基于TCN-GRU模型的短期负荷预测方法［J］. 电力工程技术，2021，40（3）：66-71.［12］ GENG G C，HE Y，ZHANG J，et al. Short-term power load forecasting based on PSO-optimized VMDTCNattention mechanism［J］. Energies，2023，16（12）：4616.［13］ 张晓凤，王秀英. 灰狼优化算法研究综述［J］. 计算机科学，2019，46（3）：30-38.［14］ 刘丽桑，郭凯琪，陈健，等. 基于VMD-MWOA-ELM的日前光伏功率预测［J］. 福建工程学院学报，2023，21（3）：269-276.［15］ 钟璐, 杨华, 李世林, 等. 基于二次分解和JSO-TCN模型的短期光伏功率预测 ［J］. 水力发电, 2024, 50(11): 74-80, 105.［16］ GAO H B，QIU S，FANG J，et al. Shortterm prediction of PV power based on combined modal decomposition and NARXLSTMLightGBM［J］. Sustainability，2023，15（10）：8266.

相似文献/References:

[1]刘丽桑,郭凯琪,陈健,等.基于VMD-MWOA-ELM的日前光伏功率预测[J].福建理工大学学报,2023,21(03):269.[doi:10.3969/j.issn.1672-4348.2023.03.011]
　LIU Lisang,GUO Kaiqi,CHEN Jian,et al.Prediction of day-ahead photovoltaic power generation based on VMD-MWOA-ELM[J].Journal of Fujian University of Technology;,2023,21(04):269.[doi:10.3969/j.issn.1672-4348.2023.03.011]
[2]朱希,林俊德,施翔宇,等.基于VMD-SSA及误差补偿的风电功率超短期预测[J].福建理工大学学报,2023,21(06):573.[doi:10.3969/j.issn.1672-4348.2023.06.010]
　ZHU Xi,LIN Junde,SHI Xiangyu,et al.Ultra-short-term prediction of wind powerbased on VMD-SSA and error compensation[J].Journal of Fujian University of Technology;,2023,21(04):573.[doi:10.3969/j.issn.1672-4348.2023.06.010]
[3]董志文,苏晶晶.基于VMD-MTF-CNN的故障电弧检测方法[J].福建理工大学学报,2024,22(04):371.[doi:10.3969/j.issn.2097-3853.2024.04.010]
　DONG Zhiwen,SU Jingjing.Arc fault detection method based on VMD-MTF-CNN[J].Journal of Fujian University of Technology;,2024,22(04):371.[doi:10.3969/j.issn.2097-3853.2024.04.010]

更新日期/Last Update: 2025-08-25