Smart and Sustainable Grids Using Data-Driven Methods; Considering Artificial Neural Networks and Decision Trees

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.

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Smart and Sustainable Grids Using Data-Driven Methods; Considering Artificial Neural Networks and Decision Trees

Cited by 2 publications

References 93 publications

Stochastic optimization for energy economics and renewable sources management: A case study of solar energy in digital twin

Stochastic optimization for energy economics and renewable sources management: A case study of solar energy in digital twin

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

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