Short-term Load Forecasting Based on Improved Manifold Regularization Extreme Learning Machine

LI Donghui; YAN Zhenlin; YAO Lele; ZHENG Hongyu

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Short-term Load Forecasting Based on Improved Manifold Regularization Extreme Learning Machine

更新时间：2025-12-08

- Short-term Load Forecasting Based on Improved Manifold Regularization Extreme Learning Machine
- Vol. 42, Issue 7, Pages: 2092-2099(2016)
- 作者机构：
  
  天津大学电气与自动化工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：31 July 2016，
  
  Published：2016
- 稿件说明：
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LI Donghui, YAN Zhenlin, YAO Lele, et al. Short-term Load Forecasting Based on Improved Manifold Regularization Extreme Learning Machine[J]. 2016, 42(7): 2092-2099.
DOI：

LI Donghui, YAN Zhenlin, YAO Lele, et al. Short-term Load Forecasting Based on Improved Manifold Regularization Extreme Learning Machine[J]. 2016, 42(7): 2092-2099. DOI：

摘要

为提高短期电力负荷预测的精度与效率

提出一种改进流形正则化极限学习机的短期电力负荷预测方法;首先

为了改善极限学习机（ELM）的泛化性能与效率

并解决随机初始化参数导致极限学习机存在的潜在问题

采用流形正则化理论优化极限学习机;其次

针对流形正则化极限学习机中参数的选择

以及流形正则化极限学习机隐层节点选择的问题

提出将贝叶斯优化算法（BOA）融入到流形正则化极限学习机中以优化流形正则化极限学习机（MRELM）。最后

通过实验数据分析

改进流形正则化极限学习机预测方法将预测平均相对误差降低到了1.903%

30次实验的平均相对误差的方差降低到了1.9‰

平均单次运行时间降低到了6.113 s。

Abstract

To improve the accuracy and efficiency of short-term load forecasting

we proposed a load forecasting method based on an improved manifold regularization extreme learning machine. Firstly

to improve the generalization performance and efficiency

and to solve the potential problem of ELM caused by random initialization parameters

Manifold Regularization Extreme Learning Machine（MRELM） was put forward. Secondly

in view of the problems about the selection of parameters in MRELM and the selection on hidden layer nodes of MRELM

Bayesian optimization algorithm（BOA） was introduced to the MRELM to optimize the MRELM. According to analysis of experimental data based on the BOA-MRELM

the mean relative error（MRE） is decreased by 1.903%. After 30 experiments

the variance of the MRE is decreased by 1.9‰. What’s more

the average single running time can be reduced to 6.113 s.

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