考虑VMD残差量和优化BiLSTM的短期负荷预测

谢煜轩; 王红君; 岳有军; 赵辉; 谢煜轩; 王红君; 岳有军; 赵辉

doi:10.13306/j.1672-3813.2024.04.021

复杂系统与复杂性科学 >

2024 , Vol. 21 >Issue 4: 149 - 156

DOI: https://doi.org/10.13306/j.1672-3813.2024.04.021

研究论文

考虑VMD残差量和优化BiLSTM的短期负荷预测

谢煜轩 ,
王红君 ,
岳有军 ,
赵辉 ,
谢煜轩 ,
王红君 ,
岳有军 ,
赵辉

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天津理工大学天津市复杂控制理论与应用重点实验室,天津 300384

谢煜轩(1998-),男,河南巩义人,硕士,主要研究方向为复杂系统智能控制理论及应用。

收稿日期: 2023-06-01

修回日期: 2023-07-06

网络出版日期: 2025-01-03

基金资助

天津市自然科学基金重点项目(08JCZDJC18600);天津市教委重点基金项目(2006ZD32)

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Short-term Load Forecasting Considering VMD Residuals and Optimizing BiLSTM

XIE Yuxuan ,
WANG Hongjun ,
YUE Youjun ,
ZHAO Hui ,
XIE Yuxuan ,
WANG Hongjun ,
YUE Youjun ,
ZHAO Hui

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School of Automation Tianjin Complex Control Theory and Application of Key Laboratory, Tianjin University of Technology, Tianjin 300384, China

Received date: 2023-06-01

Revised date: 2023-07-06

Online published: 2025-01-03

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摘要

为进一步提高短期负荷预测精度,提出了一种基于变分模态分解(VMD)并考虑VMD残差量和改进北方苍鹰算法(INGO)优化双向长短时记忆(BiLSTM)网络的短期负荷预测方法。首先利用VMD将历史负荷数据分解为多个本征模分量(IMFs)和一个残差量。再将各IMF和残差量以及相关气象参数分别构建BiLSTM模型进行预测。为避免因超参数选取不佳对预测精度的影响,采用INGO对BiLSTM的隐含层节点、训练次数、学习率进行优化。最后将预测结果叠加得出最终结果。通过具体算例分析,将本文采用方法与其他方法对比,具有较高的预测精度,验证了本文方法的有效性。

关键词： 短期负荷预测; 变分模态分解; 北方苍鹰算法; 双向长短时记忆网络

本文引用格式

谢煜轩 , 王红君 , 岳有军 , 赵辉 , 谢煜轩 , 王红君 , 岳有军 , 赵辉 . 考虑VMD残差量和优化BiLSTM的短期负荷预测[J]. 复杂系统与复杂性科学, 2024 , 21(4) : 149 -156 . DOI: 10.13306/j.1672-3813.2024.04.021

Abstract

This study proposes a new method to improve short-term load forecasting accuracy. The method is based on Variational Modal Decomposition (VMD) with consideration of VMD residuals and an Improved Northern Eagle Algorithm (INGO) optimized Bi-directional Long Short Term Memory (BiLSTM) network. The VMD is used to decompose historical load data into multiple eigenmode components (IMFs) and a residual quantity. The BiLSTM model is then constructed separately for each IMF and residual, as well as the associated meteorological parameters. To avoid the impact of poorly selected hyperparameters on prediction accuracy, the INGO algorithm optimizes the implied layer nodes, training times, and learning rates of the BiLSTM. Last but not least, the prediction results are superimposed to obtain the final results. By analyzing specific cases, this paper′s method has demonstrated a higher prediction precision when compared to alternative methods. This validation confirms the effectiveness of the method presented in this article.

Key words： short-term load forecasting; variational mode decomposition; northern goshawk optimization; bi-directional long short-trem memory

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