Year: 2024
Type: article
Abstract: Large consumers of electricity mainly purchase electricity on the wholesale electricity and capacitymarket, for example, industrial enterprises of ferrous metallurgy. For the production of products, largeindustrial enterprises daily order hourly volumes of electricity consumption for two days in advance, ifnecessary, enterprises have the right to send adjusted values for the day preceding the day of consumption.At the same time, for deviations from the planned hourly volumes, enterprises incur additional costs,which are included in the electricity tariff. One of the most important factors that affect the forecasting ofhourly electricity consumption are: the variety of types of main and auxiliary equipment, the capacities ofelectricity consumers carrying out the technological processes of the enterprise; the intensity of productionload and operating modes depending on the production of the product range; the frequent use ofhours of maximum electric power during the Days; energy-intensive production. To build forecast data fortime series, a model is built to predict hourly electricity consumption by an industrial enterprise and has alarge number of input data that have a probabilistic component. Consideration of various methods forforecasting time series of electricity consumption of industrial enterprises seems to be an urgent scientificand technical task. This is due to the requirements of minimization, firstly, of jumps and failures in theoperation of generating capacities of the energy system of the region in which the enterprise is located(since the load, for example, of ferrous metallurgy enterprises can reach up to 10% of the total consumptionof the region), and secondly, additional costs associated with the purchase/sale of volumes of electricityconsumed in excess of the application/unused in case of inaccurate planning of hourly volumes of electricityconsumed, which are included in the electricity tariff.
Authors: I. V. Emanov, N. E. Sergeev
Institution: Southern Federal University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4406337168