基于样本熵与BWO优化的VMD-DELM短期多特征负荷预测

doi:10.13306/j.1672-3813.2026.02.014

复杂系统与复杂性科学

2026, Vol. 23

Issue (2): 109-117 DOI: 10.13306/j.1672-3813.2026.02.014

智能算法

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基于样本熵与BWO优化的VMD-DELM短期多特征负荷预测

马星河^a, 徐磊^a, 马永强^b

河南理工大学 a.电气工程与自动化学院; b.软件学院,河南焦作 454000

Short-term Multi-feature Load Forecasting Using Sample Entropy and BWO in VMD-DELM

MA Xinghe^a, XÜ Lei^a, MA Yongqiang^b

a. School of Electrical Engineering and Automation; b. School of Software, Henan Polytechnic University, Jiaozuo 454000, China

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摘要为提高负荷预测精度,提出一种基于白鲸优化算法(Beluga Whale Optimization,BWO),样本熵(Sample Entropy,SE),变分模态分解(Variational Mode Decomposition,VMD),深度极限学习机(Deep Extreme Learning Machine,DELM)的短期负荷预测模型。首先,基于最小化各分量局部样本熵的原则,使用BWO优化算法对VMD的模态分解层数与惩罚因子迭代寻优,将电力负荷序列分解为高精度负荷子序列。其次,针对分解的负荷子序列建立DELM负荷预测模型并通过BWO算法对模型初始权值和阈值寻优。然后,通过皮尔逊相关系数法对输入的特征进行筛选。将每个分量预测得到的结果重构得到最终预测结果。最后,从澳大利亚某地真实负荷数据为例进行实验。实验结果证明:该负荷预测模型的平均百分比误差MAPE降低到0.169 1%。对比主流预测模型预测结果更加准确,验证了该模型有较高的准确性。

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	马星河
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关键词 ：短期负荷预测, 样本熵, 变分模态分解, 白鲸优化算法, 深度极限学习机

Abstract：To enhance load forecasting accuracy, a short-term load forecasting model incorporating Beluga Whale Optimization (BWO), Sample Entropy (SE), Variational Mode Decomposition (VMD), and Deep Extreme Learning Machine (DELM) is proposed. Initially, based on the principle of minimizing the local sample entropy of each component, using the BWO optimization algorithm to iteratively optimize the mode decomposition number and penalty factor of VMD, thereby decomposes the power load sequence into high-precision sub-sequences. Subsequently, a DELM load forecasting model is constructed for the decomposed load sequences, with initial weights and thresholds optimized using BWO. Feature selection and extraction are then performed using the Pearson coefficient method on input features. Finally, experimental validation is conducted using real load data from a specific location in Australia. The experimental results demonstrate a reduction of the mean absolute percentage error (MAPE) to 16.9% for the load forecasting model. Comparative analysis against mainstream forecasting models confirms its superior accuracy, validating its effectiveness.

Key words： short-term load prediction sample entropy VMD BWO DELM

收稿日期: 2024-04-23 出版日期: 2026-05-19

:	TM715
	TM183

基金资助:河南省高等学校重点科研项目计划(23A520037)

通讯作者: 徐磊(1999-),男,河南南阳人,硕士研究生,主要研究方向为人工智能在电力系统中的应用。

作者简介: 马星河(1979-),男,河南焦作人,博士,副教授,主要研究方向为计算机控制与新型变换器。

引用本文:

马星河, 徐磊, 马永强. 基于样本熵与BWO优化的VMD-DELM短期多特征负荷预测[J]. 复杂系统与复杂性科学, 2026, 23(2): 109-117.
MA Xinghe, XÜ Lei, MA Yongqiang. Short-term Multi-feature Load Forecasting Using Sample Entropy and BWO in VMD-DELM[J]. Complex Systems and Complexity Science, 2026, 23(2): 109-117.

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https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2026.02.014 或 https://fzkx.qdu.edu.cn/CN/Y2026/V23/I2/109

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[1]	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.

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