Forecasting the Electricity Consumption at Industrial Enterprises Using an Autoregressive Integrated Moving Average Model
DOI:
https://doi.org/10.24160/1993-6982-2021-6-11-19Keywords:
prediction error, unsteadiness, autoregressive order, moving average order, time series difference order, model adequacyAbstract
The problems concerned with forecasting the amount of electricity consumed by industrial enterprises are of utmost importance. An effective way of resolving conflicts between an industrial enterprise and a power supply company and preventing the occurrence of additional costs is to perform maximally accurate prediction of electricity consumption parameters, which can be achieved by reducing them for a contractual settlement period. Electricity consumption forecasting is of importance both from the technological and economical points of view, and its completeness has a direct effect on improving the competitiveness of manufactured products. This, in turn, is determined by a significant fraction of electricity expenditures in the enterprise output net cost. In determining the forecasted indicators of electricity consumption by industrial enterprises, it is advisable to use modern high-precision forecasting methods that ensure the minimum prediction error. The article addresses matters concerned with forecasting the electricity consumption by industrial enterprises with applying the autoregressive integrated moving average (ARIMA) method and using the Python 3.9 programming language (with involvement of the following software packages: statsmodels, numpy, pandas, pmdarima, and matplotlib) taking a metallurgical enterprise in the Republic of Uzbekistan as an example. An outline flowchart of electricity consumption prediction algorithm using the autoregressive integrated moving average method has been developed. The electricity consumption value predicted using the developed model is compared with the actual electricity consumption data. The adequacy of the developed models is validated by low absolute and relative errors between the actual and predicted data. An analysis of prediction error values has shown that the low prediction error in percentage terms is due to adequately developed mathematical models of electricity consumption parameters, due to which these can be applied to forecast the electricity consumption parameters at ferrous metallurgy enterprises.
References
2. Rahmonov I.U. Existing Methods and Approaches to Forecasting Electric Consumption at Industrial Enterprises // Intern. J. Advanced Research in Sci., Eng. and Technol. 2019. V. 6. Iss. 2. Pp. 8183—8185.
3. Пальчиков А.С. Существующие способы прогнозирования электропотребления объектов в металлургической отрасли // Современные научные исследования и инновации. 2012. № 9. [Электрон. ресурс] www.web.snauka.ru/issues/2012/09/16877 (дата обращения 12.06.2021).
4. Преимущества и недостатки моделей ARIMA [Электрон. ресурс] www.studme.org/41020/ekonomika/preimuschestva_nedostatki_modeley_arima (дата обращения 12.06.2021).
5. Трофимов П.Ю., Носков В.Ю. Прогнозирование временных рядов методом ARIMA // Теплотехника и информатика в образовании, науке и производстве: Сб. докладов VI Всерос. науч.-практ. конф. студентов, аспирантов и молодых учёных. Екатеринбург: Изд-во УрФУ, 2017. С. 260—262.
6. Иткина А.Я. Временные ряды. Моделирование процессов типа ARIMA (p, d, q) [Электрон. ресурс] www.docplayer.com/26841530-Itkina-a-ya-vremennye-ryady-modelirovanie-processov-tipa-arima-p-d-q-ekonometricheskiy-paket-eviews-pozvolyaet-dovolno-legko-modelirovat-sluchaynye.html (дата обращения 12.06.2021).
7. Prabhakaran S. ARIMA Model — Complete Guide to Time Series Forecasting in Python [Электрон. ресурс] www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/ (дата обращения 12.06.2021).
8. Крюков Ю.А., Чернягин Д.В.. ARIMA — модель прогнозирования значений трафика // Информационные технологии и вычислительные системы. 2011. № 2. С. 41—49.
9. Канторович Г.Г. Анализ временных рядов // Экономический журнал ВШЭ. 2002. № 3. С. 85—116.
10. Дуброва Т.А. Статистические методы прогнозирования. М.: ЮНИТИ-ДАНА, 2003.
11. Brownlee J. How to Create an ARIMA Model for Time Series Forecasting in Python [Электрон. ресурс] www.machinelearningmastery.com/arima-for-time-series-forecasting-with-python/ (дата обращения 12.06.2021).
12. Трегуб И.В., Трегуб А.В. Методика построения модели ARIMA для прогнозирования динамики временных рядов // Лесной вестник. 2011. № 5. C. 179—183.
13. Как создать модель ARIMA для прогнозирования временных рядов в Python [Электрон. ресурс] www.machinelearningmastery.ru/arima-for-time-series-forecasting-with-python/ (дата обращения 12.06.2021).
14. Петрусевич Д.А. Анализ математических моделей, используемқх для прогнозирования эконометрических временных рядов // Российский технологический журнал. 2019. Т. 7. № 2. С. 61—73.
---
Для цитирования: Рахмонов И.У., Курбонов Н.Н. Прогнозирование электропотребления промышленных предприятий с помощью модели авторегрессии проинтегрированного скользящего среднего // Вестник МЭИ. 2021. № 6. С. 11—19. DOI: 10.24160/1993-6982-2021-6-11-19
#
1. Hoshimov F.A., Rasulov A.N., Taslimov A.D., Rakhmonov I.U. The Current State of Electrometallurgy in Uzbekistan. Vienna: East West — Association Studies and Higher Education GmbH, 2017.
2. Rahmonov I.U. Existing Methods and Approaches to Forecasting Electric Consumption at Industrial Enterprises. Intern. J. Advanced Research in Sci., Eng. and Technol. 2019;6;2:8183—8185.
3. Pal'chikov A.S. Sushchestvuyushchie Sposoby Prognozirovaniya Elektropotrebleniya Ob′ektov v Metallurgicheskoy Otrasli. Sovremennye Nauchnye Issledovaniya i Innovatsii. 2012;9. [Elektron. Resurs] www.web.snauka.ru/issues/2012/09/16877 (Data Obrashcheniya 12.06.2021). (in Russian).
4. Preimushchestva i Nedostatki Modeley ARIMA [Elektron. Resurs] www.studme.org/41020/ekonomika/preimuschestva_nedostatki_modeley_arima (Data Obrashcheniya 12.06.2021). (in Russian).
5. Trofimov P.Yu., Noskov V.Yu. Prognozirovanie Vremennykh Ryadov Metodom ARIMA. Teplotekhnika i Informatika v Obrazovanii, Nauke i Proizvodstve: Sb. Dokladov VI Vseros. Nauch.-prakt. Konf. Studentov, Aspirantov i Molodykh Uchenykh. Ekaterinburg: Izd-vo UrFU, 2017:260—262. (in Russian).
6. Itkina A.Ya. Vremennye Ryady. Modelirovanie Protsessov Tipa ARIMA (p, d, q) [Elektron. Resurs] www.docplayer.com/26841530-Itkina-a-ya-vremennye-ryady-modelirovanie-processov-tipa-arima-p-d-q-ekonometricheskiy-paket-eviews-pozvolyaet-dovolno-legko-modelirovat-sluchaynye.html (Data Obrashcheniya 12.06.2021). (in Russian).
7. Prabhakaran S. ARIMA Model — Complete Guide to Time Series Forecasting in Python [Elektron. Resurs] www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/ (Data Obrashcheniya 12.06.2021).
8. Kryukov Yu.A., Chernyagin D.V.. ARIMA — Model' Prognozirovaniya Znacheniy Trafika. Informatsionnye Tekhnologii i Vychislitel'nye Sistemy. 2011;2:41—49. (in Russian).
9. Kantorovich G.G. Analiz Vremennykh Ryadov. Ekonomicheskiy Zhurnal VSHE. 2002;3:85—116. (in Russian).
10. Dubrova T.A. Statisticheskie Metody Prognozirovaniya. M.: YUNITI-DANA, 2003. (in Russian).
11. Brownlee J. How to Create an ARIMA Model for Time Series Forecasting in Python [Elektron. Resurs] www.machinelearningmastery.com/arima-for-time-series-forecasting-with-python/ (Data Obrashcheniya 12.06.2021).
12. Tregub I.V., Tregub A.V. Metodika Postroeniya Modeli ARIMA dlya Progno-zirovaniya Dinamiki Vremennykh Ryadov. Lesnoy Vestnik. 2011;5:179—183. (in Russian).
13. Kak Sozdat' Model' ARIMA dlya Prognozirovaniya Vremennykh Ryadov v Python [Elektron. Resurs] www.machinelearningmastery.ru/arima-for-time-series-forecasting-with-python/ (Data Obrashcheniya 12.06.2021). (in Russian).
14. Petrusevich D.A. Analiz Matematicheskikh Modeley, Ispol'zuemқkh dlya Prognozirovaniya Ekonometricheskikh Vremennқkh Ryadov. Rossiyskiy Tekhnologicheskiy Zhurnal. 2019;7;2:61—73. (in Russian).
---
For citation: Rakhmonov I.U., Kurbonov N.N. Forecasting the Electricity Consumption at Industrial Enterprises Using an Autoregressive Integrated Moving Average Model. Bulletin of MPEI. 2021;6:11—19. (in Russian). DOI: 10.24160/1993-6982-2021-6-11-19
