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A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil

Author

Listed:
  • Marlon Mesquita Lopes Cabreira

    (Department of Mathematics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil)

  • Felipe Leite Coelho da Silva

    (Department of Mathematics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
    Postgraduate Program in Mathematical and Computational Modeling, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil)

  • Josiane da Silva Cordeiro

    (Department of Mathematics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
    Postgraduate Program in Mathematical and Computational Modeling, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil)

  • Ronald Miguel Serrano Hernández

    (Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru)

  • Paulo Canas Rodrigues

    (Department of Statistics, Federal University of Bahia, Salvador 40170-115, Brazil)

  • Javier Linkolk López-Gonzales

    (Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru)

Abstract

The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

Suggested Citation

  • Marlon Mesquita Lopes Cabreira & Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro & Ronald Miguel Serrano Hernández & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2024. "A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil," Energies, MDPI, vol. 17(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3200-:d:1425416
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    References listed on IDEAS

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    Cited by:

    1. Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro & Kleyton da Costa & Nemias Saboya & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2025. "Time series forecasting via integrating a filtering method: an application to electricity consumption," Computational Statistics, Springer, vol. 40(9), pages 5023-5042, December.
    2. Laís Domingues Leonel & Mateus Henrique Balan & Luiz Armando Steinle Camargo & Dorel Soares Ramos & Roberto Castro & Felipe Serachiani Clemente, 2024. "Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function," Energies, MDPI, vol. 17(21), pages 1-19, October.

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