融合多元经验模态分解和贝叶斯优化的多任务多元负荷预测方法

陈梓恺; 张勇; 宋贤芳; 陈志鹏

doi:doi:10.19912/j.0254-0096.tynxb.2024-2138

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融合多元经验模态分解和贝叶斯优化的多任务多元负荷预测方法

更新时间：2026-07-22

- 融合多元经验模态分解和贝叶斯优化的多任务多元负荷预测方法
- Vol. 47, Issue 4, Pages: 38-45(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2138
  CLC： TP18
- Online First：08 May 2026，
  
  Published：2026
- 稿件说明：
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陈梓恺, 张勇, 宋贤芳, et al. 融合多元经验模态分解和贝叶斯优化的多任务多元负荷预测方法[J]. 2026, 47(4): 38-45.
DOI：

陈梓恺, 张勇, 宋贤芳, et al. 融合多元经验模态分解和贝叶斯优化的多任务多元负荷预测方法[J]. 2026, 47(4): 38-45. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2138.

摘要

提出一种融合多元经验模态分解和贝叶斯优化的多任务多元负荷预测方法（MEMD-BO-MMFM）。首先

给出一种融合多元经验模态分解和近似熵的多元负荷数据重组方法

将每种负荷的分解量重组为随机、周期和趋势3类分量;随后

给出一种基于贝叶斯优化的门控循环单元-多任务学习模型训练方法

为每类分量确定最为适合的网络超参数

并在建模过程中充分挖掘不同负荷之间的共性规律

实现多负荷预测任务的协同优化。实验结果表明

所提方法在预测精度和计算效率方面均优于传统方法。

Abstract

This paper proposes a multi-task multivariate load forecasting method integrating multivariate empirical mode decomposition and Bayesian optimization （MEMD-BO-MMFM）. Firstly

a multi load data recombination method that integrates multi empirical mode decomposition and approximate entropy is proposed

which recombines the decomposition of each load into three categories： random

periodic

and trend components; Subsequently

a Bayesian optimization-based training framework for the gated recurrent unit-multi-task learning （GRU-MTL） model is developed

aiming to identify the optimal network hyperparameters for each category of decomposed components. During the modeling process

the method fully leverages the underlying commonalities across different load types to facilitate the collaborative optimization of multiple load forecasting tasks. The experimental results show that the proposed method outperforms traditional methods in both prediction accuracy and computational efficiency.

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