Short-Term Electrical Load Forecasting Based on XGBoost Model

Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Oleg Evgenievich Babikov and Iliya Krastev Iliev ()
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Hristo Ivanov Beloev: Department Agricultural Machinery, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria
Stanislav Radikovich Saitov: Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia
Antonina Andreevna Filimonova: Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia
Natalia Dmitrievna Chichirova: Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia
Oleg Evgenievich Babikov: Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia
Iliya Krastev Iliev: Department of Heat, Hydraulics and Environmental Engineering, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria

Energies, 2025, vol. 18, issue 19, 1-32

Abstract: Forecasting electricity consumption is one of the most important scientific and practical tasks in the field of electric power engineering. The forecast accuracy directly impacts the operational efficiency of the entire power system and the performance of electricity markets. This paper proposes algorithms for source data preprocessing and tuning XGBoost models to obtain the most accurate forecast profiles. The initial data included hourly electricity consumption volumes and meteorological conditions in the power system of the Republic of Tatarstan for the period from 2013 to 2025. The novelty of the study lies in defining and justifying the optimal model training period and developing a new evaluation metric for assessing model efficiency—financial losses in Balancing Energy Market operations. It was shown that the optimal depth of the training dataset is 10 years. It was also demonstrated that the use of traditional metrics (MAE, MAPE, MSE, etc.) as loss functions during training does not always yield the most effective model for market conditions. The MAPE, MAE, and financial loss values for the most accurate model, evaluated on validation data from the first 5 months of 2025, were 1.411%, 38.487 MWh, and 16,726,062 RUR, respectively. Meanwhile, the metrics for the most commercially effective model were 1.464%, 39.912 MWh, and 15,961,596 RUR, respectively.

Keywords: short-term load forecasting (STLF); wholesale electricity market; balancing energy markets; machine learning; data preprocessing; XGBoost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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