Application of Non-Parametric and Forecasting Models for the Sustainable Development of Energy Resources in Brazil

Gabriela Mayumi Saiki (), André Luiz Marques Serrano (), Gabriel Arquelau Pimenta Rodrigues, Guilherme Dantas Bispo, Vinícius Pereira Gonçalves, Clóvis Neumann, Robson de Oliveira Albuquerque and Carlos Alberto Schuch Bork
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Gabriela Mayumi Saiki: Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
André Luiz Marques Serrano: Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Gabriel Arquelau Pimenta Rodrigues: Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Guilherme Dantas Bispo: Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Vinícius Pereira Gonçalves: Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Clóvis Neumann: Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Robson de Oliveira Albuquerque: Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Carlos Alberto Schuch Bork: Brazilian National Confederation of Industry (CNI), Brasilia 70040-903, Brazil

Resources, 2024, vol. 13, issue 11, 1-29

Abstract: To achieve Sustainable Development Goal 7 (SDG7) and improve energy management efficiency, it is essential to develop models and methods to forecast and enhance the process accurately. These tools are crucial in shaping the national policymakers’ strategies and planning decisions. This study utilizes data envelopment analysis (DEA) and bootstrap computational methods to evaluate Brazil’s energy efficiency from 2004 to 2023. Additionally, it compares seasonal autoregressive integrated moving average (SARIMA) models and autoregressive integrated moving average (ARIMA) forecasting models to predict the variables’ trends for 2030. One significant contribution of this study is the development of a methodology to assess Brazil’s energy efficiency, considering environmental and economic factors to formulate results. These results can help create policies to make SDG7 a reality and advance Brazil’s energy strategies. According to the study results, the annual energy consumption rate is projected to increase by an average of 2.1% by 2030, which is accompanied by a trend of GDP growth. By utilizing existing technologies in the country, it is possible to reduce electricity consumption costs by an average of 30.58% while still maintaining the same GDP value. This demonstrates that sustainable development and adopting alternatives to minimize the increase in energy consumption can substantially impact Brazil’s energy sector, improving process efficiency and the profitability of the Brazilian industry.

Keywords: data envelopment analysis (DEA); energy; forecasting; supply; time series (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2024
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