Short-term load forecasting based on improved manifold regularization extreme learning machine

被引：0
|
作者：
Li D. [1 ]
Yan Z. [1 ]
Yao L. [1 ]
Zheng H. [1 ]
机构：
[1] School of Electrical Engineering and Automation, Tianjin University, Tianjin
来源：
Yan, Zhenlin (2014yzl@sina.com) | 2016年 / Science Press卷 / 42期
关键词：
Bayesian optimization algorithm (BOA); Extreme learning machine (ELM); Manifold regularization; Mean relative error (MRE); Short-term load forecasting; Variance;
D O I：
10.13336/j.1003-6520.hve.20160713009
中图分类号：
学科分类号：
摘要：
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. © 2016, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：2092 / 2099
页数：7
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