Probabilistic Demand Forecasting in the Southeast Region of the Mexican Power System Using Machine Learning Methods

Ivan Itai Bernal Lara, Roberto Jair Lorenzo Diaz, María de los Ángeles Sánchez Galván, Jaime Robles García, Mohamed Badaoui (), David Romero Romero and Rodolfo Alfonso Moreno Flores
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Ivan Itai Bernal Lara: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico
Roberto Jair Lorenzo Diaz: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico
María de los Ángeles Sánchez Galván: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico
Jaime Robles García: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico
Mohamed Badaoui: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico
David Romero Romero: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico
Rodolfo Alfonso Moreno Flores: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico

Forecasting, 2025, vol. 7, issue 3, 1-16

Abstract: This paper focuses on electricity demand forecasting and its uncertainty representation using a hybrid machine learning (ML) model in the eastern control area of southeastern Mexico. In this case, different sources of uncertainty are integrated by applying the Bootstrap method, which adds the characteristics of stochastic noise, resulting in a hybrid probabilistic and ML model in the form of a time series. The proposed methodology addresses a function density probability, which is the generalized of extreme values obtained from the errors of the ML model; however, it is adaptable and independent and simulates the variability that may arise due to unforeseen events. Results indicate that for a five-day forecast using only demand data, the proposed model achieves a Mean Absolute Percentage Error (MAPE) of 4.358%; however, incorporating temperature increases the MAPE to 5.123% due to growing uncertainty. In contrast, a day-ahead forecast, including temperature, improves accuracy, reducing MAPE to 1.644%. The stochastic noise component enhances probabilistic modeling, yielding a MAPE of 3.042% with and 2.073% without temperature in five-day forecasts. Therefore, the proposed model proves useful for regions with high demand variability, such as southeastern Mexico, while maintaining accuracy over longer time horizons.

Keywords: electricity demand forecasting; probabilistic forecasting; short-term forecasting; stochastic noise; uncertainty modeling; XGBoost (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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