Forecasting the consumption of electricity by enterprises

I. D. Morgoev ; A. E. Dzgoev ; R. V. Klyuev ; A. D. Morgoeva

News of the Kabardin-Balkar scientific center of RAS, no. 3 (2022), pp. 9-20.

Résumé

The paper considers the problem of planning the demand for electricity for sales organizations using intellectual data analysis. Due to the fact that planning of consumption volumes opens up new economic opportunities for enterprises when entering the wholesale electricity market, forecasting is a necessary economic lever for making optimal decisions in the process of planning and allocating resources. Thus, the purpose of the study was to obtain a reliable forecast of electricity consumption. It should be noted that the forecasting of electricity consumption will improve the efficiency of management decisions for both electric grid companies and individual energy-intensive consumers (industrial enterprises). In the course of the study, a set of methods of scientific knowledge, including machine learning methods, was applied. As a result, several machine learning models were built, with the help of which a forecast of electricity consumption was made. A comparative analysis of the results of forecasting by quality metrics was carried out: the average absolute error of the forecast and the coefficient of determination. The best values of these metrics were obtained using a model based on the CatBoostRegressor algorithm. Therefore, in order to predict power consumption, the use of the developed model, in our opinion, will be most appropriate.

Détail

Keywords: electric power industry, machine learning, clustering, forecasting.
Mots-clés : regression

Bibliographie

[1] V. N. Volkova, Kozlov V. N., System analysis, decision-making, Vvsshaja shkola, Moskva, 2004, 616 pp. (In Russian)

[2] I. D. Morgoev, A. E. Dzgoev, R. V. Klyuev et al., “Modern ways to combat commercial losses in the electric power industry”, Energy of the future digital transformation, Proceedings of the 2nd Scientific-practical conference, LGTU, Lipeck, 2021, 181–185 (In Russian)

[3] D. V. Antonenkov, P. V. Matrenin, “Ensemble and neural network machine learning models for short-term load forecasting of open cast mining companies”, Electrotechnical systems and complexes, 2021, no. 3 (52), 57–65 (In Russian) | DOI

[4] N. A. Serebryakov, “Analysis of factors affecting the electricity consumption of a delivery point cluster default provider”, Proceedings of Irkutsk State Technical University, 2020, no. 2 (151), 366–381 (In Russian) | DOI

[5] A. D. Morgoeva, I. D. Morgoev, R. V. Klyuev, V. I. Lyashenko, “Forecasting the load on the power grid as a way to effectively manage the consumption of electrical energy”, News of Higher Educational Institutions of the Chernozem Region, 2021, no. 4 (66), 39–51 (In Russian) | DOI

[6] Alfonso Gonzalez-Briones, Sigeru Omatu, Mohd Saberi Mohamad, “Machine Learning Models for Electricity Consumption Forecasting: A Review”, 2nd International Conference on Computer Applications Information Security (ICCAIS) (Riyadh, Saudi Arabia), IEEE, 2019 | DOI | MR

[7] Dougherty C., Introduction to econometrics, Oxford University Press, New York, 1999, 402 pp.

[8] S. Rashka, V. Mirdzhalili, Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, Packt, Birmingham, Mumbai, 2020, 848 pp.

[9] C. Albon, Machine Learning with Python Cookbook Practical Solutions from Preprocessing to Deep Learning, Beijing–Boston–Farnham–Sebastopol–Tokyo, 2019, 384 pp.