Accounting the factor of randomity of social processes in prediction of demand for electric energy

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Authors:


D.V.Yatsenko, orcid.org/0000-0001-6702-569X, National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.A.Popov, orcid.org/0000-0003-3484-4597, National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.P.Rozen, orcid.org/0000-0002-0440-4251, National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.I.Zamulko, orcid.org/0000-0001-8018-6332, National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.V.Adanikov, orcid.org/0000-0003-2773-244X, TapOk IT-company, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (2): 067 - 072

https://doi.org/10.33271/nvngu/2022-2/067



Abstract:



Purpose.
Taking into account the factor of randomness of social processes when forecasting the demand for electric energy to reduce the error.


Methodology.
Apparatus of mathematical statistics, linear programming methods, fuzzy set theory and expert assessment methods, scale theory, Bayesian approach to forecasting models, computer modeling.


Findings.
The dynamics of consumption of electric energy for different periods of time is analyzed, the influence of the pandemic factor on the process of formation of demand for electric energy is established. A verbal-numerical scale has been developed for a comprehensive assessment of the impact on the demand for electric energy of such a complex social phenomenon as a pandemic. A model for forecasting the demand for electrical energy was formed using the Bayesian approach and an experts assessment, which made it possible to use retrospective data on electrical energy consumption and take into account the uncertainty of the social factor influencing the pandemic.


Originality.
The model for forecasting the demand for electrical energy has been further developed, which, unlike others, takes into account the factor of randomness of social processes and a verbal-numerical scale, which makes it possible to reduce the error in predicting the consumption of electrical energy.


Practical value.
The research results are useful for enterprises specializing in the generation, transmission and distribution of electrical energy to consumers. The presented results make it possible to reduce the error in forecasting the demand for electric energy, taking into account the factor of randomness of social processes.



Keywords:
demand for electricity, forecasting, uncertainty, power system, Bayesian method, Covid-19, expert judgment

References.


1. Rychlitsky, V. (2020). Quarantine meters: how the coronavirus affected electricity consumption, 2020. Retrieved from https://www.epravda.com.ua/publications/2020/07/7/662632/.

2. Morva, G., & Diahovchenko, I. (2020). Effects of COVID-19 on the electricity sectors of Ukraine and Hungary: challenges of energy demand and renewables integration. 2020 IEEE 3rd International Conference and Workshop in buda on Electrical and Power Engineering (CANDO-EPE), 41-46. https://doi.org/10.1109/CANDO-PE51100.2020.9337785.

3. Czosnyka, M., Wnukowska, B., & Karbowa, K. (2020). Electrical energy consumption and the energy market in Poland during the COVID-19 pandemic. Progress in Applied Electrical Engineering (PAEE). 1-5. https://doi.org/10.1109/PAEE50669.2020.9158771.

4. Agdas, D., & Barooah, P. (2020). Impact of the COVID-19 Pandemic on the U.S. Electricity Demand and Supply: An Early View From Data. IEEE Access, 8, 151523-151534. https://doi.org/10.1109/ACCESS.2020.3016912.

5. Carere, F., Bragatto, T., & Santori, F. (2020). A Distribution Network during the 2020 COVID-19 Pandemic. AEIT International Annual Conference (AEIT), 1-6. https://doi.org/10.23919/AEIT50178.2020.9241191.

6. Carmon, D., Navon, A., Machlev, R., Belikov, J., & Levron, Y. (2020). Readiness of Small Energy Markets and Electric Power Grids to Global Health Crises: Lessons From the COVID-19 Pandemic. IEEE Access, 8, 127234-127243. https://doi.org/10.1109/ACCESS.2020.3008929.

7. Agdas, D., & Barooah, P. (2020). Impact of the COVID-19 pandemic on the U.S. electricity demand and supply: An early view from data. IEEE Access, 8, 151523-151534. https://doi.org/10.1109/ACCESS.2020.3016912.

8. Baker, S., Bloom, N., Davis, S., & Terry, S. (2020). COVID-induced economic uncertainty. NBER Working Paper, (26983), 1-16. https://doi.org/10.3386/w26983.

9. Narajewski, M., & Ziel, F. (2020). Changes in Electricity Demand Pattern in Europe Due to COVID-19 Shutdowns. IAEE Energy Forum/Covid-19, arXiv 2020, 44-47. https://doi.org/arXiv:2004.14864v2.

10. Werth, A., Gravino, P., & Prevedello, G. (2020). Impact analysis of COVID-19 responses on energy grid dynamics in Europe. Applied Energy, 281, 116045. https://doi.org/10.1016/j.apenergy.2020.116045.

11. Rahman, M.A., & Sarker, B.R. (2012). A Bayesian approach to forecast intermittent demand for seasonal products. International Journal of Industrial and Systems Engineering, 11(1), 137-153. https://doi.org/10.1504/IJISE.2012.046660.

12. de Barros, M.V., Possamai, O., Veriano Oliveira Dalla Valentina,L., & de Oliveira, M.A. (2015). Analysis of time to market complexity: A case study of application of Bayesian networks as a forecasting tool. International Conference on Industrial Engineering and Systems Management (IESM), 1197-1204. https://doi.org/10.1109/IESM.2015.7380305.

13. Vu, D. (2015). A combination model based on a neural network autoregression and Bayesian network to forecast for avoiding brown plant hopper. International Conference on Advanced Technologies for Communications (ATC), 220-225. https://doi.org/10.1109/ATC.2015.738832314.

14. Rozen, V.P., Chermalyh, A.V., & Bychkovskij, A.S. (2015). Forecasting mean monthly wind speed using Bayesian forecasting approach. Electrotechnic and computer systems, (27), 151-156.

 

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