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Forecasting Ethanol and Gasoline Consumption in Brazil: Advanced Temporal Models for Sustainable Energy Management

Author

Listed:
  • André Luiz Marques Serrano

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

  • Patricia Helena dos Santos Martins

    (Department of Economics, University of Brasília, Federal District, Brasília 70910-900, Brazil)

  • Guilherme Fay Vergara

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

  • Guilherme Dantas Bispo

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

  • Gabriel Arquelau Pimenta Rodrigues

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

  • Letícia Rezende Mosquéra

    (Department of Economics, University of Brasília, Federal District, Brasília 70910-900, Brazil)

  • Matheus Noschang de Oliveira

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

  • Clovis Neumann

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

  • Maria Gabriela Mendonça Peixoto

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

  • Vinícius Pereira Gonçalves

    (Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil)

Abstract

The sustainable management of energy resources is fundamental in addressing global environmental and economic challenges, particularly when considering biofuels such as ethanol and gasoline. This study evaluates advanced forecasting models to predict consumption trends for these fuels in Brazil. The models analyzed include ARIMA/SARIMA, Holt–Winters, ETS, TBATS, Facebook Prophet, Uber Orbit, N-BEATS, and TFT. By leveraging datasets spanning 72, 144, and 263 months, the study aims to assess the effectiveness of these models in capturing complex temporal consumption patterns. Uber Orbit exhibited the highest accuracy in forecasting ethanol consumption among the evaluated models, achieving a mean absolute percentage error (MAPE) of 6.77%. Meanwhile, the TBATS model demonstrated superior performance for gasoline consumption, with a MAPE of 3.22%. Our models have achieved more accurate predictions than other compared works, suggesting ethanol demand is more dynamic and underlining the potential of advanced time–series models to enhance the precision of energy consumption forecasts. This study contributes to more effective resource planning by improving predictive accuracy, enabling data-driven policy making, optimizing resource allocation, and advancing sustainable energy management practices. These results support Brazil’s energy sector and provide a framework for sustainable decision making that could be applied globally.

Suggested Citation

  • André Luiz Marques Serrano & Patricia Helena dos Santos Martins & Guilherme Fay Vergara & Guilherme Dantas Bispo & Gabriel Arquelau Pimenta Rodrigues & Letícia Rezende Mosquéra & Matheus Noschang de O, 2025. "Forecasting Ethanol and Gasoline Consumption in Brazil: Advanced Temporal Models for Sustainable Energy Management," Energies, MDPI, vol. 18(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1501-:d:1614757
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    References listed on IDEAS

    as
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    3. Letícia Rezende Mosquéra & Matheus Noschang de Oliveira & Patricia Helena dos Santos Martins & Guilherme Dantas Bispo & Raquel Valadares Borges & André Luiz Marques Serrano & Fabiano Mezadre Pompermay, 2024. "Biofuel Dynamics in Brazil: Ethanol–Gasoline Price Threshold Analysis for Consumer Preference," Energies, MDPI, vol. 17(21), pages 1-25, October.
    4. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
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