| Issue |
SHS Web Conf.
Volume 107, 2021
9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2021)
|
|
|---|---|---|
| Article Number | 05004 | |
| Number of page(s) | 7 | |
| Section | Information Systems and Technologies in Economics | |
| DOI | https://doi.org/10.1051/shsconf/202110705004 | |
| Published online | 24 May 2021 | |
The development of energy consumption forecasting model for a metallurgical enterprise
Zaporizhzhia Polytechnic National University, 64 Zhukovskoho Str., Zaporizhzhia, 69063, Ukraine
* e-mail: abaka111060@gmail.com
** e-mail: yuskivolesya@rambler.ru
*** e-mail: hoveringphoenix@gmail.com
**** e-mail: elina_vt@ukr.net
† e-mail: rjabenkoae@gmail.com
An up-to-date issue of a modern metallurgical enterprise is the increase of its energy efficiency, which is related, first of all, with energy saving. Therefore, the purpose of this paper is to develop a model for forecasting the metallurgical enterprise power system consumption and its experimental testing based on the PJSC “Electrometallurgical plant “Dniprospetsstal” named after A. M. Kuzmin data. In order to build a forecasting model, a neural network apparatus in the MATLAB system was used and it was done in two stages. At the first stage, as an experiments series result, the optimal architecture and algorithm of neural network training were determined. In the second stage, the dependence of the modeling graphs load error from the influence of daily consumption graphs is identified. The MATLAB software package has been adapted for the needs of “Dniprospetsstal” named after A. M. Kuzmin. Neural networks designed in this way can be used to solve applied issues of electrometallurgy, in particular, the long-term estimation of time series of hourly power for the 24 hours ahead.
© The Authors, published by EDP Sciences, 2021
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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