Accounting the factor of randomity of social processes in prediction of demand for electric energy
- Details
- Category: Content №2 2022
- Last Updated on 30 April 2022
- Published on 30 November -0001
- Hits: 4062
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.
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.
Newer news items:
- Mathematical 3D-modeling in the formation process of the overhauled surfaces in the repair industry - 30/04/2022 13:42
- Simulation of cargo delivery by road carrier: case study of the transportation company - 30/04/2022 13:42
- Non-relational approach to developing knowledge bases of expert system prototype - 30/04/2022 13:42
- State policy of Ukraine in the sphere of environmental protection in the context of European integration - 30/04/2022 13:42
- The impact of financial development on accelerating the environmental degradation in Bangladesh - 30/04/2022 13:42
- Research into the properties of poured asphalt from electric furnace slag aggregate - 30/04/2022 13:42
- Improvement of concrete and building mortar technology using secondary mineral resources - 30/04/2022 13:41
- Research on chemical composition of secondary copper anodes obtained from aqueous residues of refining process - 30/04/2022 13:41
- Geological and economic risk assessment for territories of hazardous geological and technogenic processes (exemplified by Solotvyno township) - 30/04/2022 13:41
- Information and analytical support for making well-informed administrative decisions in civil protection system - 30/04/2022 13:41
Older news items:
- Electromechanical system of turbomechanism when using an alternative source of electric energy - 30/04/2022 13:41
- A new approach to improving the sensitivity of earthing relay and reducing the over-voltage in 6 kV grid of open-pit mines - 30/04/2022 13:41
- Thermodynamics of the developing contact heating of a process liquid - 30/04/2022 13:41
- Establishment of a suitable reference system for the geodetic horizontal control network in hydroelectric construction in Viet Nam - 30/04/2022 13:41
- Determination of the stability of a three-layer shell of a traveling wheel with light filler - 30/04/2022 13:41
- Spatial control over ultrasonic cleaning of mining equipment using a phased array technology - 30/04/2022 13:41
- Treatment of formation water at oil fields using granular filters with varying particle sizes - 30/04/2022 13:41
- Research into heap leaching technology of gold-bearing ores at the Vasilkovsky gold ore deposit (Kazakhstan) - 30/04/2022 13:41
- On the possible role of magmatism in the atasu type strathiform mineralization (Central Kazakhstan) - 30/04/2022 13:41
- On the material of the cromlech-crepidoma stone blocks of the Novooleksandrivka kurgan - 30/04/2022 13:41