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Экономико-математическое моделирование и прогнозирование электропотребления промышленного предприятия (на примере ОАО «ММК»)

Экономико-математическое моделирование и прогнозирование электропотребления промышленного предприятия (на примере ОАО «ММК»)

Abstract

The problem of forecasting of a power consumption of the large industrial enterprise for the purpose of minimization of the additional expenses connected with a shortage and search of capacity is considered. For forecast construction are used models of neural networks and the models constructed by a SSA-method. Conclusions become and recommendations are made.

Рассматривается проблема прогнозирования электропотребления крупного промышленного предприятия с целью минимизации дополнительных расходов, связанных с недобором и перебором мощности. Для построения прогноза используются нейросетевые модели и модели, построенные методом «Гусеница»-SSA (Singular Spectrum Analysis). Делаются выводы и даются рекомендации.

Keywords

ЭЛЕКТРОПОТРЕБЛЕНИЕ ПРОМЫШЛЕННОГО ПРЕДПРИЯТИЯ, ПРОГНОЗ ЭЛЕКТРОПОТРЕБЛЕНИЯ, ПРОГНОЗ НА ОСНОВЕ НЕЙРОСЕТЕВОЙ МОДЕЛИ, МЕТОД "ГУСЕНИЦА"-SSA, АНАЛИЗ И ПРОГНОЗ ВРЕМЕННЫХ РЯДОВ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average