Issue |
MATEC Web Conf.
Volume 239, 2018
Siberian Transport Forum - TransSiberia 2018
|
|
---|---|---|
Article Number | 01056 | |
Number of page(s) | 5 | |
Section | Mechanical and Energy Transport Systems | |
DOI | https://doi.org/10.1051/matecconf/201823901056 | |
Published online | 27 November 2018 |
Integrated approach to the planning of energy consumption by non-traction railway consumers
Ural State University of Railway Transport, Kolmogorova Street, 66, Yekaterinburg, 620034, Russia
* Corresponding author: akovalev@usurt.ru
Non-traction railway load consumes a significant amount of electricity. Russian Railways supplies electricity not only to its structural units but also to other consumers. Private houses, individual entrepreneurs and small production facilities located near the railway are powered by the RZD. It is very important for the company to plan consumption for non- traction needs. The subject of the study in the paper is a systematic approach to the planning of power consumption using the mathematical apparatus of artificial neural networks, correlation analysis and the method of expert assessments. The method of expert assessments allows identifying the most significant factors that affect the consumption of electricity. It is necessary to attract experienced professionals in the field of electricity, working at a particular enterprise. They are able to determine with a high degree of accuracy those factors that have a significant impact on the consumption of the organization. Correlation analysis allows you to mathematically check the degree of influence of a single factor on the resulting value. The apparatus of artificial neural networks allows building a forecast of power consumption, taking into account the influence of external factors. The authors propose to use a systematic approach to the planning of power consumption. It is necessary to combine three tools: the method of expert assessments, correlation analysis and artificial neural networks. The combination of these tools will improve the accuracy of power consumption planning and, as a result, will lead to increased economic efficiency due to the rational consumption of electricity.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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