+7 925 966 4690, 9am6pm (GMT+3), Monday – Friday
ИД «Финансы и кредит»






Financial Analytics: Science and Experience

A fuzzy approach to the regional electric power system's stability monitoring based on socially available information

Vol. 17, Iss. 1, MARCH 2024

Received: 23 October 2023

Received in revised form: 12 November 2023

Accepted: 24 November 2023

Available online: 29 February 2024


JEL Classification: C39, L94

Pages: 4–36


Dmitrii G. RODIONOV Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, Russian Federation


Evgenii A. KONNIKOV Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, Russian Federation


Oleg Yu. BORISOV Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, Russian Federation

ORCID id: not available

Dar'ya A. KRYZHKO Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, Russian Federation


Irina A. SMIRNOVA Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, Russian Federation

ORCID id: not available

Subject. This article deals with the issues related to the stability of the region's electricity system.
Objectives. The article aims to develop an original approach to monitoring the stability of the region's electric power system.
Methods. For the study, we used a fuzzy logic approach.
Results. The article proposes an algorithm for monitoring the stability of the region's electric power system based on socially accessible information, based on a fuzzy approach. The proposed forecasting research algorithm consists of five successive steps. The result of the forecasting was a polynomial function reflecting the change in the parameter of the load on the system over time.
Conclusions and Relevance. The consumption indicator over time is unstable, prone to sharp changes both negatively and positively, which may be due to the specifics of the formation of demand for electricity, where the consumer's decision is of key importance. The results of the study can be used to develop strategies for regional electricity consumption systems, and can also be implemented in the practice of specific electric power enterprises as part of making forecasts for energy consumption.

Keywords: electric energy system, continuous monitoring, system stability, decision tree


  1. Mounir N., Ouadi H., Jrhilifa I. Short-term Electric Load Forecasting Using an EMD-BI-LSTM Approach for Smart Grid Energy Management System. Energy and Buildings, 2023, vol. 288, no. 113022. URL: Link
  2. De Oliveira E.M., Oliveira F.L.C. Forecasting Mid-Long Term Electric Energy Consumption Through Bagging ARIMA and Exponential Smoothing Methods. Energy, 2018, vol. 144, pp. 776–788. URL: Link
  3. Uzlu E., Kankal M., Akpınar A. et al. Estimates of Energy Consumption in Turkey Using Neural Networks with the Teaching–Learning-Based Optimization Algorithm. Energy, 2014, vol. 75, pp. 295–303. URL: Link
  4. Hamzacebi C., Es H.A. Forecasting the Annual Electricity Consumption of Turkey Using an Optimized Grey Model. Energy, 2014, vol. 70, pp. 165–171. URL: Link
  5. Bianco V., Manca O., Nardini S. Electricity Consumption Forecasting in Italy Using Linear Regression Models. Energy, 2009, vol. 34, iss. 9, pp. 1413–1421. URL: Link
  6. Chaima E., Lian Jijian, Chao Ma et al. Long-term Electricity Demand Scenarios for Malawi's Electric Power System. Energy for Sustainable Development, 2023, vol. 73, pp. 23–38. URL: Link
  7. Castelli M., Vanneschi L., De Felice M. Forecasting Short-term Electricity Consumption Using a Semantics-Based Genetic Programming Framework: The South Italy Case. Energy Economics, 2015, vol. 47, pp. 37–41. URL: Link
  8. Kaboli S.Hr.A., Fallahpour A., Selvaraj J., Rahim N.A. Long-term Electrical Energy Consumption Formulating and Forecasting via Optimized Gene Expression Programming. Energy, 2017, vol. 126, pp. 144–164. URL: Link
  9. Meira E., Lila M.F., Oliveira F.L.C. A Novel Reconciliation Approach for Hierarchical Electricity Consumption Forecasting Based on Resistant Regression. Energy, 2023, vol. 269, no. 126794. URL: Link
  10. Jianjun Wang, Li Li, Dongxiao Niu, Zhongfu Tan. An Annual Load Forecasting Model Based on Support Vector Regression with Differential Evolution Algorithm. Applied Energy, 2012, vol. 94, pp. 65–70. URL: Link
  11. Arbuzov A.D. [Method of monitoring the dynamics of clusters of socio-technical systems based on fuzzy cognitive approach]. Mezhdunarodnyi zhurnal informatsionnykh tekhnologii i energoeffektivnosti, 2021, vol. 6, no. 1, pp. 23–33. (In Russ.) URL: Link
  12. Kochetkova T.S. [A comprehensive assessment of business processes of enterprises: fuzzy-set approach]. Sovremennye naukoemkie tekhnologii. Regional'noe prilozhenie = Modern High Technologies. Regional Application, 2016, no. 4, pp. 78–83. URL: Link (In Russ.)
  13. Zhibao Wang, Lijie Wei, Xiaoping Zhang, Guangzhi Qi. Impact of Demographic Age Structure on Energy Consumption Structure: Evidence from Population Aging in Mainland China. Energy, 2023, vol. 273, no. 127226. URL: Link
  14. Rodionov D.G., Korotkova E.A., Kryzhko D.A. et al. [Transformation of the ecological environment of socio-economic systems under the influence of information environment factors]. Ekonomicheskie nauki = Economic Sciences, 2021, no. 8, pp. 98–111. URL: Link (In Russ.)
  15. Davankov A.Yu., Dvinin D.Yu., Postnikov E.A. [Methodological tools for the assessment of ecological and socio-economic environment in the region within the limits of the sustainability of biosphere]. Ekonomika regiona = Economy of Region, 2016, vol. 12, iss. 4, pp. 1029–1039. URL: Link metodicheskiy-instrumentariy-otsenki-sotsio-ekologo-ekonomicheskoy-sredy-regiona-v-granitsah-ustoychivosti-biosfery?ysclid=lp8ao10le7159771993 (In Russ.)
  16. Alekseev V.A., Rodionov D.G., Konnikov E.A. [Condition and development vector of the world nuclear energy]. Ekonomicheskie nauki = Economic Sciences, 2022, no. 10, pp. 155–161. URL: Link (In Russ.)
  17. Rodionov D.G., Kulagina N.A., Lagutenkov A.A. [Main trends in the international market energy resources: facts and lessons of the COVID-19 pandemic]. Vestnik Altaiskoi akademii ekonomiki i prava = Bulletin of Altai Academy of Economics and Law, 2022, no. 2-2, pp. 244–250. URL: Link (In Russ.)
  18. Zatonskii A.V., Sirotina N.A., Yanchenko T.V. [On the approximation of the factors of the differential model of the socio-economic system]. Russian Journal of Education and Psychology, 2012, no. 11. (In Russ.) URL: Link
  19. Zenkov V.V. [Applying an approximation of the Anderson Discriminant Function and support vector machines for solving some classification tasks]. Avtomatika i telemekhanika = Automation and Remote Control, 2020, vol. 81, no. 1, pp. 147–160. (In Russ.) URL: Link
  20. Barbashova E.V., Gaidamakina I.V., Pol'shakova N.V. [Forecasting in short time series: methodological and methodical aspects]. Vestnik agrarnoi nauki = Bulletin of Agrarian Science, 2020, no. 2, pp. 84–98. URL: Link prognozirovanie-v-korotkih-vremennyh-ryadah-metodologicheskie-i-metodicheskie-aspekty?ysclid=loofz2lgwt243949464 (In Russ.)
  21. Pchelintsev S.Yu. [Comparative analysis of deep learning frameworks]. Informatsionno-ekonomicheskie aspekty standartizatsii i tekhnicheskogo regulirovaniya, 2020, no. 1. (In Russ.) URL: Link
  22. Senin A.S., Lyasnikov N.V. [Making management decisions in crisis situations based on neural network "decision tree"]. Ekonomika i sotsium: sovremennye modeli razvitiya, 2019, vol. 9, no. 1. (In Russ.) URL: Link
  23. Kovalenko A.V., Gavrilov A.A., Teunaev D.M. et al. [Using methods of multidimensional statistical analysis to assess the socio-economic development of urban districts of the Krasnodar Region]. Politematicheskii setevoi elektronnyi nauchnyi zhurnal Kubanskogo gosudarstvennogo agrarnogo universiteta, 2002, no. 155. (In Russ.) URL: Link
  24. Chernyshova G.Yu., Samarkina E.A. [Data mining methods for financial time series forecasting]. Izvestiya Saratovskogo universiteta. Novaya seriya. Seriya: Ekonomika. Upravlenie. Pravo = Izvestiya of Saratov University. New Series. Series: Economics. Management. Law, 2019, vol. 19, no. 2, pp. 181–188. URL: Link article/n/metody-intellektualnogo-analiza-dannyh-dlya-prognozirovaniya-finansovyh-vremennyh-ryadov?ysclid=lp8cfhwatg252439255 (In Russ.)

View all articles of issue


ISSN 2311-8768 (Online)
ISSN 2073-4484 (Print)

Journal current issue

Vol. 17, Iss. 1
March 2024