Short-term load forecasting based on VMD and DBN

YAO Haiqiang; ZHANG Tao

doi:10.13873/J.1000-9787(2021)05-0139-03

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Short-term load forecasting based on VMD and DBN

更新时间：2025-09-11

- Short-term load forecasting based on VMD and DBN
- Issue 5, Pages: 139-141+149(2021)
- 作者机构：
  
  安徽大学电气工程与自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2021)05-0139-03
  CLC： TM715;TP183
- Published：2021
- 稿件说明：
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YAO Haiqiang, ZHANG Tao. Short-term load forecasting based on VMD and DBN[J]. 2021, (5): 139-141+149.
DOI：

YAO Haiqiang, ZHANG Tao. Short-term load forecasting based on VMD and DBN[J]. 2021, (5): 139-141+149. DOI： 10.13873/J.1000-9787(2021)05-0139-03.

摘要

短期负荷预测作为电力调度中的关键一环

对电网未来的发展具有深远的意义。提出一种基于变分模态分解(VMD)和深度置信网络(DBN)的短期负荷预测模型。首先

采用VMD算法将负荷数据分解成不同的本征模态函数(IMF);然后结合DBN网络对每个IMF进行预测;最后

叠加每个部分的预测结果

得到VMD-DBN模型的预测结果。实验结果表明:相对于单一使用DBN方法以及经验模态分解(EMD)组合DBN方法

本文所提方法在预测精度上有显著提升。

Abstract

Short-term load forecasting

as a key link in power dispatching

has far-reaching sig-nificance for future development of power grid.A short-term load forecasting model based on variational mode decomposition（VMD）and deep belief networks（DBN） is proposed.Firstly

VMD algorithm is used to decompose load data into different intrinsic mode functions（IMF）.Secondly

predict each IMF with the help of DBN network.Finally

superimpose the prediction results of each part to obtain prediction results of VMD-DBN model.The experimental results show that compared with simply used DBN method and empirical mode decomposition（EMD）-DBN method

the proposed VMD-DBN method has a significant improvement in prediction precision.

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