IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i6p1501-d1614757.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/6/1501/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/6/1501/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eirini Stergiou & Nikos Rigas & Eftychia Zaroutieri & Konstantinos Kounetas, 2023. "Energy, renewable and technical efficiency convergence: a global evidence," Economic Change and Restructuring, Springer, vol. 56(3), pages 1601-1628, June.
    2. Sikiru, Surajudeen & Abioye, Kunmi Joshua & Adedayo, Habeeb Bolaji & Adebukola, Sikiru Yesirat & Soleimani, Hassan & Anar, M., 2024. "Technology projection in biofuel production using agricultural waste materials as a source of energy sustainability: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    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.
    5. Canabarro, N.I. & Silva-Ortiz, P. & Nogueira, L.A.H. & Cantarella, H. & Maciel-Filho, R. & Souza, G.M., 2023. "Sustainability assessment of ethanol and biodiesel production in Argentina, Brazil, Colombia, and Guatemala," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alejandro Zacarías & Mario R. Grijalva & José de Jesús Rubio & Guerlin Romage & Violeta Y. Mena & Raúl Hernández & Ignacio Carvajal & Alicia Flores & Orlando Guarneros & Brayan A. Rodríguez, 2025. "Improvement Efficiency and Emission Reduction in Used Cars for Developing Regions Using Gasoline–Bioethanol Blends," Energies, MDPI, vol. 18(3), pages 1-19, January.
    2. Irene M. Zarco-Soto & Fco. Javier Zarco-Soto & Pedro J. Zarco-Periñán, 2021. "Influence of Population Income on Energy Consumption and CO 2 Emissions in Buildings of Cities," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    3. Niraj Buyo & Akbar Sheikh-Akbari & Farrukh Saleem, 2025. "An Ensemble Approach to Predict a Sustainable Energy Plan for London Households," Sustainability, MDPI, vol. 17(2), pages 1-30, January.
    4. Anatolijs Borodinecs & Kristina Lebedeva & Natalja Sidenko & Aleksejs Prozuments, 2022. "Enhancement of Chiller Performance by Water Distribution on the Adiabatic Cooling Pad’s Mesh Surface," Clean Technol., MDPI, vol. 4(3), pages 1-19, July.
    5. Khadija Sherece Usher & Benjamin Craig McLellan, 2024. "Inherent Risk Analysis of Power Supply Management: Case of Belize’s System Operator and Third-Party Actors," Energies, MDPI, vol. 18(1), pages 1-35, December.
    6. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    7. Napolitano, Oreste & Foresti, Pasquale & Kounetas, Konstantinos & Spagnolo, Nicola, 2023. "The impact of energy, renewable and CO2 emissions efficiency on countries’ productivity," Energy Economics, Elsevier, vol. 125(C).
    8. Rial, Rafael Cardoso, 2024. "Biofuels versus climate change: Exploring potentials and challenges in the energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    9. Erkul, Abdullah & Türköz, Kumru, 2024. "Green growth governance and total factor energy efficiency: Economic growth constraint and policy implementation in OECD countries," Renewable Energy, Elsevier, vol. 235(C).
    10. Erkan Erdogdu, 2025. "Restructuring energy markets for net-zero: the role of market-based solutions," Economic Change and Restructuring, Springer, vol. 58(2), pages 1-40, April.
    11. Bhandari, Ramchandra & Subedi, Subodh, 2023. "Evaluation of surplus hydroelectricity potential in Nepal until 2040 and its use for hydrogen production via electrolysis," Renewable Energy, Elsevier, vol. 212(C), pages 403-414.
    12. Gang Chen & Qingchang Hu & Jin Wang & Xu Wang & Yuyu Zhu, 2023. "Machine-Learning-Based Electric Power Forecasting," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    13. Galusnyak, Stefan Cristian & Petrescu, Letitia & Cosprundan, Ariadna Maria & Cormos, Calin-Cristian, 2024. "Biodiesel production using various methanol sources and catalytic routes: A techno-environmental analysis," Renewable Energy, Elsevier, vol. 232(C).
    14. D’Errico, Maria Chiara, 2024. "Sustainable economic growth and energy security nexus: A stochastic frontier analysis across OECD countries," Energy Economics, Elsevier, vol. 132(C).
    15. José Ignacio García-Lajara & Miguel Ángel Reyes-Belmonte, 2022. "Liquefied Natural Gas and Hydrogen Regasification Terminal Design through Neural Network Estimated Demand for the Canary Islands," Energies, MDPI, vol. 15(22), pages 1-24, November.
    16. Manuel Jaramillo & Diego Carrión, 2022. "An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19," Energies, MDPI, vol. 15(22), pages 1-19, November.
    17. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
    18. Jerzy Rembeza & Grzegorz Przekota, 2022. "Influence of the Industry’s Output on Electricity Prices: Comparison of the Nord Pool and HUPX Markets," Energies, MDPI, vol. 15(16), pages 1-15, August.
    19. Sen, Doruk & Hamurcuoglu, K. Irem & Ersoy, Melisa Z. & Tunç, K.M. Murat & Günay, M. Erdem, 2023. "Forecasting long-term world annual natural gas production by machine learning," Resources Policy, Elsevier, vol. 80(C).
    20. Bibi Ibrahim & Luis Rabelo & Edgar Gutierrez-Franco & Nicolas Clavijo-Buritica, 2022. "Machine Learning for Short-Term Load Forecasting in Smart Grids," Energies, MDPI, vol. 15(21), pages 1-19, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1501-:d:1614757. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.