计算机与数字工程2025，Vol.53Issue(1)：96-102,7.DOI:10.3969/j.issn.1672-9722.2025.01.019
基于VMD-TCN的短期负荷预测方法研究
Research on Short-term Load Forecasting Based on VMD-TCN
王树东 1李润清 2曹万水2
作者信息
- 1. 兰州理工大学电气工程与信息工程学院 兰州 730050||兰州理工大学甘肃省工业控制先进控制重点实验室 兰州 730050||兰州理工大学电气与控制工程国家级实验教学示范中心 兰州 730050
- 2. 兰州理工大学电气工程与信息工程学院 兰州 730050
- 折叠
摘要
Abstract
In order to improve the prediction accuracy of the model,this paper adopts a method based on the maximal informa-tion coefficient(MIC).Sparrow algorithm(SSA)optimizes variational mode decomposition(VMD).The prediction model of Tempo-ral convolutional network and temporal pattern attention is combined.Firstly,considering the fluctuation and non-stationary of the original load signal,the VMD optimized by sparrow algorithm is used to decompose the original load sequence into different modal components,and the prediction difficulty of the neural network is reduced by sample entropy reconstruction.Considering the weath-er,electricity price and other influencing factors,MIC is adopted to screen the external features strongly associated with the current moment load signal,so as to achieve feature optimization and dimension reduction.Secondly,the decomposed modal components and the external features are screened by MIC respectively constitute the training set.Finally,the TPA-TCN model of time convolu-tion network based on time mode attention mechanism is constructed to predict.The practical example shows that the proposed pre-diction model can effectively improve the accuracy of prediction.关键词
短期负荷预测/时间卷积网络/变分模态分解/最大互信息系数/样本熵/时间模式注意力机制/麻雀算法Key words
short-term load forecasting/sequential convolutional network/variational modal decomposition/maximum mu-tual information coefficient/sample entropy/time model attention mechanism/sparrow algorithm分类
信息技术与安全科学引用本文复制引用
王树东,李润清,曹万水..基于VMD-TCN的短期负荷预测方法研究[J].计算机与数字工程,2025,53(1):96-102,7.
