Short-term Load Combination Forecasting Model Based on Variational Nonlinear FM Mode Decomposition and TCN-TPA-LSTM

WANG Boyu; WEN Zhong; ZHOU Xiang; ZHAO Di; YAN Wenwen; QIN Zhiyin

doi:10.19725/j.cnki.1007-2322.2023.0250

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Short-term Load Combination Forecasting Model Based on Variational Nonlinear FM Mode Decomposition and TCN-TPA-LSTM

更新时间：2025-10-22

- Short-term Load Combination Forecasting Model Based on Variational Nonlinear FM Mode Decomposition and TCN-TPA-LSTM
- Vol. 42, Issue 5, Pages: 891-900(2025)
- 作者机构：
  
  三峡大学电气与新能源学院,湖北省,宜昌市,443002
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0250
  CLC： 5
- Published：2025
- 稿件说明：
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WANG Boyu, WEN Zhong, ZHOU Xiang, et al. Short-term Load Combination Forecasting Model Based on Variational Nonlinear FM Mode Decomposition and TCN-TPA-LSTM[J]. 2025, 42(5): 891-900.
DOI：

WANG Boyu, WEN Zhong, ZHOU Xiang, et al. Short-term Load Combination Forecasting Model Based on Variational Nonlinear FM Mode Decomposition and TCN-TPA-LSTM[J]. 2025, 42(5): 891-900. DOI： 10.19725/j.cnki.1007-2322.2023.0250.

摘要

随着新型电力系统的发展，电力负荷“双高双峰”特性愈发明显，可靠准确的负荷预测对电力系统运行规划至关重要。为更精准地预测电力负荷，提出基于MIC-VNCMD-TCN-TPA-LSTM的短期电力负荷组合预测模型。采用最大信息系数(maximal information coefficient

MIC)理论对负荷与气象信息进行非线性耦合分析，选取关键信息。引入变分非线性调频模态分解(variational nonlinear chirp mode decomposition

VNCMD)处理非线性非平稳负荷数据，将其分解为相应分量。在此基础上，构建TCN-TPA-LSTM组合预测模型，根据各分量预测评价指标选取对应的预测模型，重组得到总体预测结果。基于某地实际电力负荷数据集的对比实验表明，所提预测方法相较于其他模型具有显著的性能优势，其预测精度和泛化能力均优于对比基准，充分验证了所提方法的有效性和技术优越性。

Abstract

The "double-high and double-peak" characteristics of power loads are becoming increasingly prominent with the advancement of new power system

necessitating reliable and accurate load forecasting for power system operation planning. To predict the power load with better accuracy

a short-term power load combination prediction model based on MIC-VNCMD-TCN-TPA-LSTM is proposed. The maximal information coefficient (MIC) theory is utilized to analyze the nonlinear coupling of load and meteorological information and identify the crucial information. Variational nonlinear chirp mode decomposition (VNCMD) is introduced to process the nonlinear non-stationary load data and decompose them into corresponding components. On this basis

a combined TCN-TPA-LSTM prediction model is constructed

and the corresponding prediction model is selected according to the prediction evaluation index of each element. Subsequently

the overall prediction results are reorganized. The actual electric load data from certain place is used as the dataset for comparison experiments

which demonstrates superior accuracy and generalization capability compared to other prediction models

thus verifying the effectiveness and superiority of the proposed method.

关键词

Keywords

references

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