The methodology of load forecasting in electric power systems: effective management algorithm

RELEVANCE. The research consists in the operational forecasting of electrical loads for both technical and economic aspects of the operation of the power system. Timely analysis of the upcoming loads allows us to determine the most efficient system operation mode, which directly affects the performance of the entire electrical complex when operating in the energy market. THE PURPOSE. To increase the accuracy of forecasting electricity consumption in the electrical complex of the grid company, providing a lower margin of error compared to current methods. METHODS. To achieve this goal, an iterative method was applied: in the Microsoft Excel environment, a sequential search and verification of existing forecasting methods was organized. RESULTS. A methodology for forecasting energy consumption by an electric utility of an energy organization has been developed. The marginal error of the proposed methodology was only 2.53%. A step-by-step algorithm for calculating the planned amount of electricity consumption by customers has been developed, ensuring consistent execution of operations to generate a comprehensive forecast. CONCLUSION. An important element of the work was the algorithm for calculating the estimated volume of electricity consumption by subscribers. This algorithm is a detailed sequence of actions necessary to implement a combined forecasting method, and also provides a systematic approach to estimating future electricity consumption, achieving a high degree of detail, which allows calculations to be performed without using specialized software, using only basic engineering calculation methods.

1. Bonchuk IA, Erokhin PM. Operational forecasting of power consumption in isolated power systems. Electricity. 2022; 1:24-34.

2. Mottaeva AB. Current trends and prospects of energy development in Russia. Bulletin of Surgut State University. 2024; 4:77-91.

3. Poluyanovich NK, Dubyago MN. Assessment of influencing factors and forecasting of power consumption in the regional energy system, taking into account its operating mode // Izvestiya Yuzhnogo federalnogo universiteta. Technical sciences. 2022. No. 2 (226). pp. 31-46.

4. Soluyanov YuI, Fedotov AI, Akhmetshin AR and others. Determination of calculated electrical loads of charging infrastructure for electric vehicles integrated into electrical installations of residential and public buildings. News of higher educational institutions. Energy problems. 2024; 6: 94-107.

5. Kapp S, Choi JK, Hong T. Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters. Renewable and Sustainable Energy Reviews. 2023; 172:113.

6. Fedotov AI, Akhmetshin AR, Fedotov EA and others. Summation of electrical loads of residential and public buildings of a residential complex. News of higher educational institutions. Energy problems. 2025; 2:76-89.

7. Bulatov YuN Kryukov AV, Suslov KV Investigation of operating modes of an isolated power supply system with controlled distributed generation units, electric power storage and propulsion load. Izvestiya vysshikh uchebnykh zavedeniy. Energy problems. 2021; 5:184-194.

8. Peng L, Wang L, Xia D, et al. Effective energy consumption forecasting using empirical wavelet transform and long short-term memory. Energy. 2022; 238:121.

9. Ghazal TM. Energy demand forecasting using fused machine learning approaches. Intelligent Automation & Soft Computing. 2022; 1:539-553.

10. 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; 288:113-122.

11. Koksharov VA. A conceptual approach to the implementation of a strategy for the efficient use of energy resources in an industrial enterprise. Innovations and investments. 2021; 7:60-64.

12. Vanin AS, Ivanov TY. Investigation of the problem of short-term forecasting of the load schedule of the electric power system. Actual problems of science and technology. 2022; 353-354.

13. Guzhov SV. On forecasting electricity demand by the energy systems of the regions of the Russian Federation using artificial neural networks. Izvestiya Transsib. 2020; 1(41):133-140.

14. Khomutov SO, Serebryakov NA. Creation of a neural network mathematical model for short-term forecasting of electrical consumption of the electrical complex of the district electrical networks 6-35 kV. Innovative transport systems and technologies. 2020; 1:80-91.

15. Brito TC, Brito MA. Forecasting of energy consumption: Artificial intelligence methods. 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), IEEE. 2022;1-4.

16. Batueva DE. Forecast of energy consumption in a changing climate. Oil Capital. 2020; 236-239.

17. Kim DY, Kim YH, Kim BS. Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear. Energy. 2021;214:119.

18. Blokhin AV. The influence of temporal and climatic factors on electricity consumption. Information technologies in science and production. 2022;42-50.

19. Rusina AG. Forecasting the daily schedule of power consumption of working days, taking into account meteorological factors for the central energy system of Mongolia. Izvestiya vysshikh uchebnykh zavedeniy. Energy problems. 2022;2:98-107.