FORECASTING ELECTRICITY CONSUMPTION BY INDUSTRIAL ENTERPRISES (REVIEW)
Keywords:
Forecasting methods, short-term forecasting of electricity, metallurgical enterprise, day-ahead forecasting, time series
Abstract
Large consumers of electricity mainly purchase electricity on the wholesale electricity and capacity
market, for example, industrial enterprises of ferrous metallurgy. For the production of products, large
industrial enterprises daily order hourly volumes of electricity consumption for two days in advance, if
necessary, 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 of
hourly electricity consumption are: the variety of types of main and auxiliary equipment, the capacities of
electricity consumers carrying out the technological processes of the enterprise; the intensity of production
load and operating modes depending on the production of the product range; the frequent use of
hours of maximum electric power during the Days; energy-intensive production. To build forecast data for
time series, a model is built to predict hourly electricity consumption by an industrial enterprise and has a
large number of input data that have a probabilistic component. Consideration of various methods for
forecasting time series of electricity consumption of industrial enterprises seems to be an urgent scientific
and technical task. This is due to the requirements of minimization, firstly, of jumps and failures in the
operation 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 consumption
of the region), and secondly, additional costs associated with the purchase/sale of volumes of electricity
consumed in excess of the application/unused in case of inaccurate planning of hourly volumes of electricity
consumed, which are included in the electricity tariff.
References
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ranking-based framework for electricity spot price forecasting using a hybrid deep neural network,
Electric Power Systems Research, 2021, Vol. 200, 107453. ISSN 0378-7796. Available at:
https://doi.org/10.1016/j.epsr.2021.107453.
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metallurgii na osnove neyro-nechetkikh algoritmov [Method for forecasting electricity consumption
volumes by a ferrous metallurgy enterprise based on neuro-fuzzy algorithms]. Available at:
https://www.dissercat.com/content/metod-prognozirovaniya-obemov-potrebleniya-elektroenergiipredpriyatiem-
chernoi-metallurgii-.
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prognozirovaniya elektropotrebleniya grupp tochek postavki elektroenergii garantiruyushchikh
postavshchikov vtorogo urovnya [Improving the accuracy of short-term forecasting of electricity consumption
of groups of electricity supply points of second-tier guaranteed suppliers], Izvestiya
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University. Georesources engineering], 2020, Vol. 331, No. 6, pp. 128-140. Available at:
https://doi.org/10.18799/24131830/2020/6/2682.
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correction using dynamic mode decomposition, Applied Energy, March 2020, Vol. 261, 114368.
Available at: https://www.sciencedirect.com/science/article/abs/pii/S0306261919320550? via%3Dihub.
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08858950. Available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-36348992080&origin=
inward&txGid=21e6fb2c3300889b3ff56d705b52239d.
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Araghi. Forecasting time series with multiple seasonal patterns, European Journal of Operational Research,
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elektricheskoy-energii-s-ispolzovaniem-neyronnyh-setey.
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using a hybrid model, Energy, 2018, Vol. 158, pp. 774-781. ISSN 0360-5442,
https://doi.org/10.1016/j.energy.2018.06.012.
27. Staroverov B.A. Teoreticheskoe obosnovanie i prakticheskaya realizatsiya sistem prognozirovaniya
elektropotrebleniya na osnove ansambley neyronnykh setey v masshtabe regional'noy setevoy
kompanii [Theoretical justification and practical implementation of electricity consumption forecasting
systems based on neural network ensembles on the scale of a regional grid company], 2023. Available
at: https://www.dissercat.com/content/teoreticheskoe-obosnovanie-i-prakticheskaya-realizatsiyasistem-
prognozirovaniya-elektropotr.
elektrostaleplavil'nom tsekhe metallurgicheskogo predpriyatiya metodami korrelyatsionnoregressionnogo
analiza [Study of electricity consumption for technological purposes in the electric
steelmaking shop of a metallurgical enterprise using correlation and regression analysis methods],
Sovremennye tekhnologii upravleniya [Modern control technologies], 2017, No 6 (78). ISSN2223-
9339. Available at: https://sovman.ru/article/7801/.
2. Mysik V.F., Zhdanov A.V. Resursy i podgotovka loma k plavke stali [Resources and preparation of
scrap for steel smelting]. 2nd ed. Ekaterinburg, 2017, 337 p.
3. Taskina A.O. Prognozirovanie tseny na elektroenergiyu na rynke «na sutki vpered» vo vtoroy tsenovoy
zone [Forecasting the price of electricity on the day-ahead market in the second price zone], 2020.
Available at: https://vital.lib.tsu.ru/vital/access/services/Download/vital:11747/SOURCE01.
4. Khank D.E., Uicheri D.U., Rayte A.Dzh. Biznes-prognozirovanie [Business forecasting]: transl. from
engl. 7th ed. Moscow: Vil'yams, 2003, 506 p.
5. Alberg D., Last M. Short-term load forecasting in smart meters with sliding window-based ARIMA
algorithms, Vietnam J. Comput. Sci., 2018, 5, pp. 241-249. Available at: https://doi.org/10.1007/
s40595-018-0119-7.
6. Rehan Jamil. Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supplydemand
analysis for the year 2030, Renewable Energy, 2020, Vol. 154, pp. 1-10. ISSN 0960-1481.
Available at: https://doi.org/10.1016/j.renene.2020.02.117.
7. Yuanyuan Wang, Jianzhou Wang, Ge Zhao, Yao Dong. Application of residual modification approach
in seasonal ARIMA for electricity demand forecasting: A case study of China, Energy Policy, 2012,
Vol. 48, pp. 284-294. ISSN 0301-4215. Available at: https://doi.org/10.1016/j.enpol.2012.05.026.
8. Xinbin Liang, Siliang Chen, Xu Zhu, Xinqiao Jin, Zhimin Du. Domain knowledge decomposition of
building energy consumption and a hybrid data-driven model for 24-h ahead predictions, Applied Energy,
2023, Vol. 344, 121244. ISSN 0306-2619. Available at: https://doi.org/10.1016/
j.apenergy.2023.121244.
9. Mehmet Bilgili, Engin Pinar. Gross electricity consumption forecasting using LSTM and SARIMA
approaches: A case study of Türkiye, Energy, 2023, Vol. 284, 128575. ISSN 0360-5442. Available at:
https://doi.org/10.1016/j.energy.2023.128575.
10. Russkov O.V. Metody prognozirovaniya neravnomernykh nestatsionarnykh vremennykh ryadov na
primere zadachi planirovaniya elektropotrebleniya promyshlennogo predpriyatiya [Methods for forecasting
uneven non-stationary time series using the example of the problem of planning electricity
consumption of an industrial enterprise], 2023. Available at: https://elib.spbstu.ru/dl/2/r23-
69.pdf/download/r23-69.pdf.
11. Nti I.K., Teimeh M., Nyarko-Boateng O. et al. Electricity load forecasting: a systematic review, Journal
of Electrical Systems and Inf. Technol., 2020, 7, 13. Available at: https://doi.org/10.1186/s43067-
020-00021-8.
12. Morgoeva A.D. Prognozirovanie potrebleniya elektricheskoy energii promyshlennym predpriyatiem s
pomoshch'yu metodov mashinnogo obucheniya [Forecasting the consumption of electrical energy by
an industrial enterprise using machine learning methods], Izvestiya Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov [Bulletin of Tomsk Polytechnic University. Georesources engineering],
2022, Vol. 333, No. 7, pp. 115-125. Available at: https://izvestiya.tpu.ru/archive/article/
view/3527/2751.
13. Morgoeva A.D., Morgoev I.D., Klyuev R.V., Khetagurov V.N., Gavrina O.A. Kratkosrochnoe
prognozirovanie elektropotrebleniya obogatitel'noy fabriki [Short-term forecasting of electricity consumption
of the processing plant], Gornyy informatsionno-analiticheskiy byulleten' [Mining information
and analytical bulletin], 2023, 5-1, pp. 157-169. Available at: https://www.giabonline.
ru/files/Data/2023/5/05_2023_157-169.pdf.
14. Klyuev R.V., Morgoeva A.D., Gavrina O.A., Bosikov I.I., Morgoev I.D. Prognozirovanie planovogo
potrebleniya elektroenergii dlya ob"edinennoy energosistemy s pomoshch'yu mashinnogo obucheniya
[Forecasting the planned electricity consumption for the unified power system using machine learning],
Zapiski Gornogo institute [Notes of the Mining Institute], 2023, No. 261. Available at:
https://cyberleninka.ru/article/n/prognozirovanie-planovogo-potrebleniya-elektroenergii-dlyaobedinennoy-
energosistemy-s-pomoschyu-mashinnogo-obucheniya.
15. Vyalkova S.A. Kratkosrochnoe prognozirovanie elektropotrebleniya Megapolisa na osnove ortogonal'nykh
razlozheniy i neyronnykh setey [Short-term forecasting of electricity consumption of a megalopolis based on
orthogonal decompositions and neural networks], 2022. Available at: https://www.npitu.
ru/upload/medialibrary/f39/hxsy86lrce075msy4mt96l1nvbqz4ux8/avtoreferat_vialkova.pdf?ysclid=lue3p
m3md4950760747.
16. Staroverov B.A. Teoreticheskoe obosnovanie i prakticheskaya realizatsiya sistem prognozirovaniya
elektropotrebleniya na osnove ansambley neyronnykh setey v masshtabe regional'noy setevoy kompanii
[Theoretical justification and practical implementation of power consumption forecasting systems based
on neural network ensembles on the scale of a regional grid company], 2023. Available at:
https://www.dissercat.com/content/teoreticheskoe-obosnovanie-i-prakticheskaya-realizatsiya-sistemprognozirovaniya-
elektropotr.
17. Alekseeva I.Yu. Kratkosrochnoe prognozirovanie elektropotrebleniya v elektroenergeticheskikh
sistemakh s ispol'zovaniem iskusstvennykh neyronnykh setey [Short-term forecasting of electricity
consumption in electric power systems using artificial neural networks]. Available at:
https://www.dissercat.com/content/kratkosrochnoe-prognozirovanie-elektropotrebleniya-velektroenergeticheskikh-
sistemakh-s-isp.
18. Zhen Shao, Qingru Zheng, Chen Liu, Shuangyan Gao, Gang Wang, Yan Chu. A feature extractionand
ranking-based framework for electricity spot price forecasting using a hybrid deep neural network,
Electric Power Systems Research, 2021, Vol. 200, 107453. ISSN 0378-7796. Available at:
https://doi.org/10.1016/j.epsr.2021.107453.
19. Bazhinov A.N. Metod prognozirovaniya ob"emov potrebleniya elektroenergii predpriyatiem chernoy
metallurgii na osnove neyro-nechetkikh algoritmov [Method for forecasting electricity consumption
volumes by a ferrous metallurgy enterprise based on neuro-fuzzy algorithms]. Available at:
https://www.dissercat.com/content/metod-prognozirovaniya-obemov-potrebleniya-elektroenergiipredpriyatiem-
chernoi-metallurgii-.
20. Khomutov S.O., Stashko V.I., Serebryakov N.A. Povyshenie tochnosti kratkosrochnogo
prognozirovaniya elektropotrebleniya grupp tochek postavki elektroenergii garantiruyushchikh
postavshchikov vtorogo urovnya [Improving the accuracy of short-term forecasting of electricity consumption
of groups of electricity supply points of second-tier guaranteed suppliers], Izvestiya
Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov [Bulletin of Tomsk Polytechnic
University. Georesources engineering], 2020, Vol. 331, No. 6, pp. 128-140. Available at:
https://doi.org/10.18799/24131830/2020/6/2682.
21. Chuang Li, Guojie Li, Keyou Wang, Bei Han. Short-term electrical load forecasting based on error
correction using dynamic mode decomposition, Applied Energy, March 2020, Vol. 261, 114368.
Available at: https://www.sciencedirect.com/science/article/abs/pii/S0306261919320550? via%3Dihub.
22. Taylor J.W., McSharry P.E. Short-term load forecasting methods: An evaluation based on European data,
IEEE Transactions on Power Systems, November 2007, Vol. 22, Issue 4, pp. 2213-2219. ISSN
08858950. Available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-36348992080&origin=
inward&txGid=21e6fb2c3300889b3ff56d705b52239d.
23. Phillip G. Gould, Anne B. Koehler, J. Keith Ord, Ralph D. Snyder, Rob J. Hyndman, Farshid Vahid-
Araghi. Forecasting time series with multiple seasonal patterns, European Journal of Operational Research,
2008, Vol. 191, Issue 1, pp. 207-222. ISSN 0377-2217, Available at: https://doi.org/10.1016/
j.ejor.2007.08.024.
24. Korotkevich M.A., Kurachinskiy V.V. Prognozirovanie elektricheskoy nagruzki energosistemy na
sleduyushchie sutki s ispol'zovaniem metoda iskusstvennykh neyronnykh setey [Forecasting the electrical
load of the power system for the next day using the method of artificial neural networks],
Energetika. Izvestiya vysshikh uchebnykh zavedeniy i energeticheskikh ob"edineniy SNG [Power Engineering.
News of higher educational institutions and energy associations of the CIS], 2010, No. 2. Available at: https://cyberleninka.ru/article/n/prognozirovanie-elektricheskoy-nagruzki-energosistemyna-
sleduyuschie-sutki-s-ispolzovaniem-metoda-iskusstvennyh-neyronnyh-setey.
25. Chernetsov V.I., Kazakovskiy E.N. Prognozirovanie potrebleniya elektricheskoy energii s
ispol'zovaniem neyronnykh setey [Forecasting the consumption of electrical energy using neural networks],
NiKa [NiKa], 2006. Available at: https://cyberleninka.ru/article/n/prognozirovaniepotrebleniya-
elektricheskoy-energii-s-ispolzovaniem-neyronnyh-setey.
26. Jinliang Zhang, Yi-Ming Wei, Dezhi Li, Zhongfu Tan, Jianhua Zhou. Short term electricity load forecasting
using a hybrid model, Energy, 2018, Vol. 158, pp. 774-781. ISSN 0360-5442,
https://doi.org/10.1016/j.energy.2018.06.012.
27. Staroverov B.A. Teoreticheskoe obosnovanie i prakticheskaya realizatsiya sistem prognozirovaniya
elektropotrebleniya na osnove ansambley neyronnykh setey v masshtabe regional'noy setevoy
kompanii [Theoretical justification and practical implementation of electricity consumption forecasting
systems based on neural network ensembles on the scale of a regional grid company], 2023. Available
at: https://www.dissercat.com/content/teoreticheskoe-obosnovanie-i-prakticheskaya-realizatsiyasistem-
prognozirovaniya-elektropotr.
