<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>At present, new knowledge about the physical nature and empirical phenomena in the field of distribution, losses, recuperation and consumption of electricity in railway transport is undergoing revolutionary changes. To date, the outputs of various types of traction electricity consumption on electric traction units (HEKV) and in the railway distribution network of the EU railway infrastructure manager (LDSŽ - Local Distribution System of Railways) have not been theoretically or practically analysed in available publications or on the scientific market. There are no empirical or experimental calculations and mathematical models at the level of mathematical approximation and intelligent prediction, i.e. at the level of preparation of technical specifications and documentation for expert software and effective ICT solutions to this problem. Research into new phenomena in the field of railway energy and research in the field of railway energy and research into expert systems and machine learning. The article describes the basic characteristics of determining the prediction of energy efficiency of traction electricity. Brings the results of the project “Research of new knowledge in the field of intelligent energy efficiency management system in rail transport using a modular neural network”, which aims to innovate procedures in solving the problem and didactic transformation of scientific content to simplify them into a subject understandable to students as interpretive and application concept of didactics of professional subjects. Transform the curriculum into students’ knowledge, attitudes and habits using teaching methods, organizational forms and material resources.</p>
</abstract>

At present, new knowledge about the physical nature and empirical phenomena in the field of distribution, losses, recuperation and consumption of electricity in railway transport is undergoing revolutionary changes. To date, the outputs of various types of traction electricity consumption on electric traction units (HEKV) and in the railway distribution network of the EU railway infrastructure manager (LDSŽ - Local Distribution System of Railways) have not been theoretically or practically analysed in available publications or on the scientific market. There are no empirical or experimental calculations and mathematical models at the level of mathematical approximation and intelligent prediction, i.e. at the level of preparation of technical specifications and documentation for expert software and effective ICT solutions to this problem. Research into new phenomena in the field of railway energy and research in the field of railway energy and research into expert systems and machine learning. The article describes the basic characteristics of determining the prediction of energy efficiency of traction electricity. Brings the results of the project “Research of new knowledge in the field of intelligent energy efficiency management system in rail transport using a modular neural network”, which aims to innovate procedures in solving the problem and didactic transformation of scientific content to simplify them into a subject understandable to students as interpretive and application concept of didactics of professional subjects. Transform the curriculum into students’ knowledge, attitudes and habits using teaching methods, organizational forms and material resources.

Improving Educational Processes Using Research of New Knowledge in the Railway Energy Efficiency Management System

Transport technic and technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

At present, new knowledge about the physical nature and empirical phenomena in the field of distribution, losses, recuperation and consumption of electricity...

Improving Educational Processes Using Research of New Knowledge in the Railway Energy Efficiency Management System

Online veröffentlicht: 17. Aug. 2022

Seitenbereich: 9 - 15

DOI: https://doi.org/10.2478/ttt-2022-0002

Schlüsselwörter
energy, carrier, prediction, algorithm, didactics, education

© 2022 Pavol Lukáč, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Improving Educational Processes Using Research of New Knowledge in the Railway Energy Efficiency Management System

Online veröffentlicht: 17. Aug. 2022

Seitenbereich: 9 - 15

DOI: https://doi.org/10.2478/ttt-2022-0002

Schlüsselwörterenergy, carrier, prediction, algorithm, didactics, education

© 2022 Pavol Lukáč, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
energy, carrier, prediction, algorithm, didactics, education