Method for forecasting and calculating the electrical load of municipal consumers under uncertainty
https://doi.org/10.21285/1814-3520-2023-3-565-573
EDN: TVYZCE
Abstract
The study addresses the conformity of actual electricity consumption to the calculated value in electric distribution networks in which municipal consumers predominate in several cities of the Chelyabinsk region. To study the conformity between the specific electrical load established by regulatory documents and the actual value per apartment according to power consumption data in several cities of the Chelyabinsk region, the average annual power consumption by municipal consumers with a specific number of apartments was analyzed over a period of 2021–2022. The correspondence analysis of the average annual electricity consumption by municipal consumers in the studied facilities was carried out using the conventional method for calculating the electrical load over the given period following the guidelines outlined in SP 256.1325800.2016. The discrepancy between the actual electrical load on the apartment and its normative value established by the acting normative documents ranged from minus 48 to 300% with respect to electricity consumption. For the considered 16 objects located in the cities of the Chelyabinsk region, the discrepancy between the actual electrical load and the established normative values was compared. For 6 apartments, this discrepancy ranged from minus 58 to 155%. To improve the accuracy of forecasting electricity consumption and calculating electrical loads in electric distribution networks with a predominance of municipal consumers, methods using a new factor were recommended. This factor involves a generalized uncertainty coefficient Ai, whose values are determined for the considered period. When using the developed methods, relative deviations in the forecast calculations are less than or equal to 10%.
About the Authors
S. Sh. Tavarov
South Ural State University (National Research University)
Russian Federation
Russian Federation
Saidjon Sh. Tavarov, Cand. Sci. (Eng.), Associate Professor of the Department of Life Safety
76, Lenin pr., Chelyabinsk 454080
A. I. Sidorov
South Ural State University (National Research University)
Russian Federation
Russian Federation
Aleksandr I. Sidorov, Dr. Sci. (Eng.), Professor, Head of the Department of Life Safety
76, Lenin pr., Chelyabinsk 454080
I. F. Suvorov
Transbaikal State University
Russian Federation
Russian Federation
Ivan F. Suvorov, Dr. Sci. (Eng.), Professor, Professor of the Department of Power Engineering
30, Aleksandro-Zavodskaya St., Chita 672039
A. B. Svyatykh
Uralenergosbyt LLC
Russian Federation
Russian Federation
Andrey B. Svyatykh, Cand. Sci. (Eng.), First Deputy General Director
26A, Entuziastov St., Chelyabinsk 454080
References
1. Repkina N.G. Research of factors affecting the accuracy prediction daily power consumption. Izvestiya vysshikh uchebnykh zavedenii. Elektromekhanika. 2015;2:41-43. (In Russ.). https://doi.org/10.17213/0136-3360-2015-2-41-43.
2. Soluyanov Yu.I., Fedotov A.I., Galitsky Yu.Ya., Chernova N.V., Akhmetshin A.R. Updating the standard specific electric loads of apartment buildings in the Republic of Tatarstan. Elektrichestvo. 2021; 6:62-71. (In Russ.). https://doi.org/10.24160/0013-5380-2021-6-62-71. EDN: RRSRRX.
3. Karpenko S.M., Karpenko N.V. Econometric modeling of energy consumption based on influence of industry factors. Energobezopasnost' i energosberezhenie = Energy Safety and Energy Economy. 2020;1:14-17. (In Russ.). https://doi.org/10.18635/2071-2219-2020-1-14-17. EDN: EDVDXC.
4. Karpenko S.M., Karpenko N.V. Analysis of dynamics and power consumption forecasting based on econometric modeling. Energobezopasnost' i energosberezhenie = Energy Safety and Energy Economy. 2020;2:20-25. (In Russ.). https://doi.org/10.18635/2071-2219-2020-2-20-25. EDN: YUCPDM.
5. Valeev G.S., Dzyuba M.A., Valeev R.G. Modeling daily load curves of the elements of 6–10 kV urban power distribution networks under conditions of limited unput information. Vestnik Yuzhno-Ural'skogo gosudarstvennogo universiteta. Seriya: Energetika = Bulletin of South Ural State University. Series: Power Engineering. 2016;16;2:23-29. (In Russ.). https://doi.org/10.14529/power160203. EDN: WCZMFX.
6. Kulikov A.L., Ilyushin P.V., Loskutov A.B., Sevost'yanov A.A. Selective monitoring of power quality indicators in distribution networks with a large share of RES generation. Elektrichestvo. 2022;7:11-23. (In Russ.). https://doi.org/10.24160/0013-5380-2022-7-11-23. EDN: TBYSOJ.
7. Fezzi C., Mosetti L. Size matters: estimation sample length and electricity price forecasting. The Energy Journal. 2020;41(4):231-254. https://doi.org/10.5547/01956574.41.4.cfez.
8. Goltsos T. E., Syntetos A. A., Van der Laan Е. Forecasting for remanufacturing: the effects of serialization. Journal of Operations Management. 2019;65(5):447-467. https://doi.org/10.1002/joom.1031.
9. Grushka-Cockayne Ya., Jose V.R.R. Combining prediction intervals in the M4 competition. International Journal of Forecasting. 2020;36(1):178-185. https://doi.org/10.1016/j.ijforecast.2019.04.015.
10. Hewamalage H., Bergmeir C., Bandara K. Recurrent neural networks for time series forecasting: current status and future directions. International Journal of Forecasting. 2021;37(1):388-427. https://doi.org/10.1016/j.ijforecast.2020.06.008.
11. Hong T., Pinson P. Energy forecasting in the big data world. International Journal of Forecasting. 2019;35(4):1387-1388. https://doi.org/10.1016/j.ijforecast.2019.05.004.
12. Issi F., Kaplan O. The determination of load profiles and power consumptions of home appliances. Energies. 2018;11(3):607. https://doi.org/10.3390/en11030607.
13. Nisha M. Demand side management for a household using resource scheduling. In: International CET Conference on Control, Communication, and Computing. 2018. https://doi.org/10.1109/CETIC4.2018.8530929.
14. Andrea G., Paoletti S., Vicino A. Models and techniques for electric load forecasting in the presence of demand response. In: IEEE Transactions on Control Systems Technology. 2014;23(3):1087-1097. https://doi.org/10.1109/TCST.2014.2361807.
15. Opriş I., Costinaş S., Ionescu C., Nistoran D. The household energy consumer in a smart metering environment. In: 9th International Symposium on Advanced Topics in Electrical Engineering. 7–9 May 2015, Bucharest. Bucharest: IEEE; 2015, р. 43-48. https://doi.org/10.1109/ATEE.2015.7133677.
16. Kim Jangkyum, Han Jaesoeb, Kim Nakyoung, Kim Minkyung, Choi Junkyun. Analysis of power usage at household and proper energy management. In: International Conference on Information and Communication Technology Convergence. 17–19 October 2018, Jeju. Jeju: IEEE; 2018, р. 450-456. https://doi.org/10.1109/ICTC.2018.8539459.
17. Senyuk M., Safaraliev M., Gulakhmadov, A., Ahyoev, J. Application of the conditional optimization method for the synthesis of the law of emergency control of a synchronous generator steam turbine operating in a complex-closed configuration power system. Mathematics. 2022;10(21):3979. https://doi.org/10.3390/math10213979.
18. Tavarov S.S., Sidorov A.I., Kalegina Y.V. Model and algorithm of electricity consumption management for household consumers in the republic of Tajikistan. Mathematical Modelling of Engineering Problems. 2020;7(4):520-526. https://doi.org/10.18280/mmep.070403.
19. Tavarov S.Sh. Method for projecting household electricity consumption in the Republic of Tajikistan. Vestnik Yuzhno-Ural'skogo gosudarstvennogo universiteta. Seriya: Energetika = Bulletin of South Ural State University. Series: Power Engineering. 2020;20(2):28-35. (In Russ.). https://doi.org/10.14529/power200203. EDN: HIOFYO.
20. Tavarov S.Sh. Artificial neural network training algorithm for factor-based prediction of electricity consumption in the household sector. Elektrichestvo. 2022;3:30-38. (In Russ.). https://doi.org/10.24160/0013-5380-2022-3-30-38. EDN: MDFRLG.
Review
For citations:
Tavarov S.Sh., Sidorov A.I., Suvorov I.F., Svyatykh A.B. Method for forecasting and calculating the electrical load of municipal consumers under uncertainty. iPolytech Journal. 2023;27(3):565-573. (In Russ.) https://doi.org/10.21285/1814-3520-2023-3-565-573. EDN: TVYZCE
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