论文摘要
Railway transportation plays an important role in modern society. As China’s massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters（e.g., loading and marshaling） of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging – REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.
论文目录
文章来源
类型: 期刊论文
作者: Guohua Xi,Xibin Zhao,Yan Liu,Jin Huang,Yangdong Deng
来源: Tsinghua Science and Technology 2019年02期
年度: 2019
分类: 工程科技Ⅱ辑
专业: 铁路运输
单位: CRRC Corporation Limited,School of Software and Key Laboratory for Information System Security, Ministry of Education (KLISS)/Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University
基金: sponsored in part by the National Natural Science Foundation of China(Nos.61872217 and 61527812),Industrial Internet Innovation&Development Project of Ministry of Industry and Information Technology of China,National Science and Technology Major Project(No.2016ZX01038101),MIIT IT funds(Research and Application of TCN Key Technologiezs)of China,the National Key Technology R&D Program(No.2015BAG14B01-02)
分类号: U268.6;U284.48
页码: 226-237
总页数: 12
文件大小: 4093K
下载量: 47