Computational Technology Resources - CCC

T. Fujimasu, D. Horikoshi, M. Yoshizawa, M. Adachi, H. Moriyama, A. Hibino, "Research Methods on Reducing Power Consumption Through Big Data Analysis in the Railway Field", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 12.6, 2024, doi:10.4203/ccc.7.12.6

Keywords: big data, rolling stock, carbon neutral, driving operation, machine learning, data driven.

Abstract

The aim of this study is to achieve a reduction in power consumption of the Tokaido Shinkansen by deriving the characteristics of operational practices that lead to lower power consumption from a large amount of field data. A method was developed to efficiently calculate and visualize the power consumption using each running data, for understanding the trends in power consumption with respect to travel time. To accurately evaluate operational practices regarding power consumption while eliminating the influence of ambient temperature and occupancy rate, another method was established to correct the effects by those factors on computed power consumption, using an equation that calculates running resistance based on actual running data. Additionally, clustering analyses were performed on notch operations to effectively understand common characteristics from various running patterns for grasping the characteristics that realize a reduction in power consumption. By combining these approaches, it became possible to derive running methods that result in lower power consumption from a large amount of field data.

download the full-text of this paper (PDF, 14 pages, 1147 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 12.6 Research Methods on Reducing Power Consumption Through Big Data Analysis in the Railway Field T. Fujimasu, D. Horikoshi, M. Yoshizawa, M. Adachi, H. Moriyama and A. Hibino Technology Research and Development Department, Central Japan Railway Company, Japan doi:10.4203/ccc.7.12.6 Full Bibliographic Reference for this paper T. Fujimasu, D. Horikoshi, M. Yoshizawa, M. Adachi, H. Moriyama, A. Hibino, "Research Methods on Reducing Power Consumption Through Big Data Analysis in the Railway Field", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 12.6, 2024, doi:10.4203/ccc.7.12.6 Keywords: big data, rolling stock, carbon neutral, driving operation, machine learning, data driven. Abstract The aim of this study is to achieve a reduction in power consumption of the Tokaido Shinkansen by deriving the characteristics of operational practices that lead to lower power consumption from a large amount of field data. A method was developed to efficiently calculate and visualize the power consumption using each running data, for understanding the trends in power consumption with respect to travel time. To accurately evaluate operational practices regarding power consumption while eliminating the influence of ambient temperature and occupancy rate, another method was established to correct the effects by those factors on computed power consumption, using an equation that calculates running resistance based on actual running data. Additionally, clustering analyses were performed on notch operations to effectively understand common characteristics from various running patterns for grasping the characteristics that realize a reduction in power consumption. By combining these approaches, it became possible to derive running methods that result in lower power consumption from a large amount of field data. download the full-text of this paper (PDF, 14 pages, 1147 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
Back to top	©Civil-Comp Limited 2023 - terms & conditions