Forecasting traction energy consumption of metro based on support vector regression

被引：0

作者：

Chen Y. ^{[1
]}

Mao B. ^{[1
]}

Bai Y. ^{[1
]}

Feng Y. ^{[1
]}

Li Z. ^{[1
]}

机构：

[1] MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2016年 / 36卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Genetic algorithm; Metro; Support vector machine; Traction energy consumption;

D O I：

10.12011/1000-6788(2016)08-2101-07

中图分类号：

学科分类号：

摘要：

Forecasting traction energy consumption of metro contributes to evaluate the energy efficiency of lines and save traction energy. Traction energy consumption is nonlinearly affected by multiple factors. In this paper, a support vector regression (SVR) model based on the historical data was proposed for metro traction energy consumption prediction. First, the influencing factors on traction energy consumption were classified to power supply system, train characteristics, track profiles, operation scheme and meteorological factors. The variable factors of metro lines were chosen as input data. Thereafter, the genetic algorithm (GA) with cross validation was applied to optimize the parameters of the SVR model. Lastly, the SVR model with the optimal parameters was utilized to forecast the traction energy consumption of a metro line. The forecasting results indicate that cross validation improves prediction accuracy of the SVR model and that the SVR model achieves higher prediction accuracy than the back-propagation neural network (BPNN) model and the multiple linear regression (MLP) model. © 2016, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

引用

页码：2101 / 2107

页数：6

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