Train Energy-Saving Scheme Optimized On Case Intelligence with Synthesis-Reasoning Technology in Urban Rail Transit

2018 3rd International Conference on Insulating Materials, Material Application and Electrical Engineering

材料科学;无线电电子学;电工学

Li, Jianyang^1 ; Wu, Hongseng^2 ; Zhu, Benkun^2

School of Electrical Engineering and Automation, Zhenjiang Institute of Technology, Zhenjiang, China^1

School of Computer and Information Engineering, Hefei University of Technology, Hefei, China^2

关键词: CBR (case based reasoning);    Domain expertise;    Energy saving schemes;    Problem-solving abilities;    RBR (rule based reasoning);    Train energy consumption;    Train operations;    Urban rail transit;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/452/3/032055/pdf
DOI  :  10.1088/1757-899X/452/3/032055

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Train energy consumption in URT has been attracted much greater concerns for it becomes more serious with the large scale operation and expansion of operation network. One of the important ways for energy-saving propulsion is to find the energy-efficient train speed curve, which is a complicated CSP (constraint satisfaction problem) with uncertainty, and cannot be solved effectively with such inconsistent constrains. The case intelligent based on CBR (case-based reasoning) is proposed in this paper for its problem-solving ability, for which the domain expertise is rich while rule knowledge deficient, to construct a flexible system integrated with efficient machine learning components and acquire the train operation preferences from the former stored cases. The experiments testing on the spot indicates that the system performs well in synthesis-reasoning, which can conquer the complexity and uncertainty of real problem from both RBR (Rule-based reasoning) and CBR, to minimize the energy consumption for train traction with punctuality and safety demands.