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

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 Oliveira, Clovis Neumann, Maria Gabriela Mendonça Peixoto and Vinícius Pereira Gonçalves
Additional contact information
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

Energies, 2025, vol. 18, issue 6, 1-20

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.

Keywords: forecasting; time–series; ethanol; gasoline; biofuel; Brazil (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
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/6/1501/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/6/1501/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:6:p:1501-:d:1614757

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().