Short-Term Load Forecasting Method Based on Ensemble Improved Extreme Learning Machine

程松 1; 闫建伟 1; 赵登福 1; 王圈 2; 王海明 2

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Short-Term Load Forecasting Method Based on Ensemble Improved Extreme Learning Machine

更新时间：2025-12-08

- Short-Term Load Forecasting Method Based on Ensemble Improved Extreme Learning Machine
- Vol. 43, Issue 2, Pages: 106-110(2009)
- 作者机构：
  
  西安交通大学电气工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM715
- Online First：10 February 2009，
  
  Published：2009
- 稿件说明：
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程松 1, 闫建伟 1, 赵登福 1, et al. Short-Term Load Forecasting Method Based on Ensemble Improved Extreme Learning Machine[J]. 2009, 43(2): 106-110.
DOI：

程松 1, 闫建伟 1, 赵登福 1, et al. Short-Term Load Forecasting Method Based on Ensemble Improved Extreme Learning Machine[J]. 2009, 43(2): 106-110. DOI：

摘要

首次将极端学习机(ELM)理论引入短期负荷预测领域

并以BFGS拟牛顿法对ELM网络左侧权值进行优化训练调整

形成基于迭代-解析的改进ELM预测模型.同时

采用集成技术Boosting算法

生成多个差异度大的改进ELM子网络

对其进行加权组合

构建了集成改进极端学习机预测模型.该模型不仅有效避免了极端学习机左侧权值随机给定的输出稳定性问题

而且克服了单一网络预测模型泛化能力较差等缺陷.

Abstract

The extreme learning machine(ELM)theory is introduced into the domain of short-term load forecasting

and an ELM forecasting model is developed based on iteration-parse via optimizing

training and rectifying the left weights with BFGS quasi-Newton method. Adopting boosting algorithm of ensemble technology

several quite different ELM child-networks are generated and weighted

and a forecasting model of ensemble improved ELM are constructed. This model avoids effectively the output unstability from the left weights given arbitrarily

and breaks such limitations as weak generalization capability of single network model.

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references

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康重庆,夏清,刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007.

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HIPPERT H S, PEDREIRA C E, SOUZA R C. Neural networks for short-term load forecasting: a review and evaluation [J]. IEEE Trans on Power Systems, 2001,16(1): 44-55.

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DRUCKER H.Improving regressors using Boosting techniques[C]∥The Fourteenth International Conference on Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1997:107-115.

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