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A Long-Term Forecasting Model of Electricity Consumption Volume on the Example of Ups of the Ural with the Help of Harmonic Analysis of a Time Series

Authors: Mokhov, V.G.; Demyanenko, T.S.;

A Long-Term Forecasting Model of Electricity Consumption Volume on the Example of Ups of the Ural with the Help of Harmonic Analysis of a Time Series

Abstract

V.G. Mokhov1, T.S. Demyanenko1 1South Ural State University, Chelyabinsk, Russian Federation E-mails: mokhovvg@susu.ru, demianenkots@susu.ru. Вениамин Геннадьевич Мохов, доктор экономических наук, профессор, кафедра ≪Математическое и компьютерное моделирование≫, Южно-Уральский государственный университет (г. Челябинск, Российская Федерация), mokhovvg@susu.ru. Татьяна Сергеевна Демьяненко, кандидат экономических наук, кафедра ≪Математическое и компьютерное моделирование≫, Южно-Уральский государственный университет (г. Челябинск, Российская Федерация), demianenkots@susu.ru. In this article, the model of forecasting of electricity consumption volume is analyzed on the basis of harmonic analysis of a time series. Previously, we establish the presence of a trend component described by a second-order polynomial. Based on the construction of the autocorrelation function, the periodicity of the time series of electricity consumption volume is proved, with periods equal to 1 year and 1 week, due to decrease in electricity consumption in summer because of increase in daylight hours and decrease in production on weekends. The model is tested on actual hourly data of United Energy System of the wholesale market for electricity and power in Russia. The model is tested for adequacy using Fisher’s criterion and the coefficient of determination. The introduction of two harmonic components (annual and weekly ones) instead of the generally accepted one reduces the approximation error for the current model from 2,25 % to 2,08 %, which provides increased energy efficiency for consumers while reducing the fines of the balancing market. The proposed scientific tools are recommended in the entity’s operating activity of electric power to forecast the major energy market parameters in order to reduce the fines by improving the accuracy of forecasts. В статье рассмотрена модель прогнозирования объемов потребления электроэнергии на основе гармонического анализа временных рядов. Ранее авторами было установлено наличие трендовой составляющей, описываемой полиномом второго порядка. На основе построения автокорреляционной функции была доказана периодичность временного ряда объема потребления электрической энергии, с периодами 1 год и 1 неделя, что обусловлено снижением электропотребления в летний период за счет увеличения светлого времени суток и снижением производства в выходные дни. Модель протестирована на фактических почасовых данных Объединенной энергосистемы Оптового рынка электроэнергии и мощности России. Модель проверена на адекватность с помощью критерия Фишера и коэффициента детерминации. Введение двух гармонических составляющих (годовой и недельной) вместо общепринятой одной снизило ошибку аппроксимации для текущей модели с 2,25 % до 2,08 %, что обеспечит повышение энергоэффективности потребителей при снижении величины штрафов балансирующего рынка. Разработанный научный инструментарий рекомендуется в операционной деятельности субъектов электроэнергетики при прогнозировании основных параметров энергетического рынка для снижения штрафных санкций за счет повышения точности прогнозов.

Keywords

УДК 001.895, энергетический рынок, energy market, основные параметры, гармонический анализ, harmonic analysis, модели прогнозирования, УДК 330.322.013, main parameters, forecasting models

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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!
2
Average
Average
Average
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gold