电测与仪表Issue(13)：71-76,110,7.
基于切换模型极限学习机的超短期负荷预测
Ultra-short term load forecasting based on switching model extreme learning machine
邓明丽 1张晶1
作者信息
- 1. 国网四川省电力公司技能培训中心，成都610065
- 折叠
摘要
Abstract
In view of the output fluctuation and model of instability in the extreme learning machine algorithm, this paper raises the method of switching model limit learning algorithm for ultra short term power load forecasting.The al-gorithm divides a plurality of the established neural network model into two parts: keeping model of small errors and updating model of large errors by switching model guidelines.To keep model has no need for online update, so as to reduce the volatility of the output of model; updating model needs to adopt stochastic methods to update online, so that the training error reaches a minimum, and the generalization ability of the model is improved.Finally, through the simulation of power load forecasting of a certain area, the predicted results show that the proposed method can im-prove the prediction speed, save computing time, and has better generalization ability and prediction accuracy.关键词
极限学习机/切换模型/负荷预测/更新模型/预测精度Key words
extreme learning machine/switching model/load forecasting/update the model/prediction accuracy分类
信息技术与安全科学引用本文复制引用
邓明丽,张晶..基于切换模型极限学习机的超短期负荷预测[J].电测与仪表,2015,(13):71-76,110,7.
