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 "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.