Public Energy Management in Brazil: Decision Analysis and Machine Learning

Article ID

Z6W2Y

Energy management, decision analysis, sustainable energy solutions, environmental impact, machine learning.

Public Energy Management in Brazil: Decision Analysis and Machine Learning

Fabricio Quadros Borges
Fabricio Quadros Borges
Bruno Alencar da Costa
Bruno Alencar da Costa
Inaldo de Souza Sampaio Filho
Inaldo de Souza Sampaio Filho
Marlis Elena Ramírez Requelme
Marlis Elena Ramírez Requelme
João Paulo Abreu Almeida
João Paulo Abreu Almeida
DOI

Abstract

The analyzes carried out by artificial intelligence must start from a complete and integrated data structure, which is classified and grouped with the intention of synergistically producing mental and predictive captures. In this perspective, the objective of this study is to analyze the possibility of contribution of artificial intelligence in guiding decision-making in the public planning of sustainable electrical matrices. The methodological procedures of this investigation, built a structure of analysis of electricity sources, based on the economic, social, environmental and technological dimensions; as well as a sectoral analysis structure of energy sustainability indicators, supported by linear correlations of an economic, social, environmental and political nature. The planning of electrical matrices, according to the inferences of this investigation, can use artificial intelligence as a strategic guide for decisions, as long as they are based on analysis structures focused on the strategic use of electricity sources and the use of sectoral and multidimensional indicators. This investigation constitutes an original contribution insofar as it discusses the possibilities of connections between artificial intelligence and the construction of electrical matrices, from the perspective of improving the decision-making process in Brazilian public planning. The discussion about these connections helps to raise subsidies for machine learning to process and develop methodologies, based on algorithms, that automate the construction of decision analysis models in the planning and sustainable construction of the use of electricity.

The analyzes carried out by artificial intelligence must start from a complete and integrated data structure, which is classified and grouped with the intention of synergistically producing mental and predictive captures. In this perspective, the objective of this study is to analyze the possibility of contribution of artificial intelligence in guiding decision-making in the public planning of sustainable electrical matrices. The methodological procedures of this investigation, built a structure of analysis of electricity sources, based on the economic, social, environmental and technological dimensions; as well as a sectoral analysis structure of energy sustainability indicators, supported by linear correlations of an economic, social, environmental and political nature. The planning of electrical matrices, according to the inferences of this investigation, can use artificial intelligence as a strategic guide for decisions, as long as they are based on analysis structures focused on the strategic use of electricity sources and the use of sectoral and multidimensional indicators. This investigation constitutes an original contribution insofar as it discusses the possibilities of connections between artificial intelligence and the construction of electrical matrices, from the perspective of improving the decision-making process in Brazilian public planning. The discussion about these connections helps to raise subsidies for machine learning to process and develop methodologies, based on algorithms, that automate the construction of decision analysis models in the planning and sustainable construction of the use of electricity.

Fabricio Quadros Borges
Fabricio Quadros Borges
Bruno Alencar da Costa
Bruno Alencar da Costa
Inaldo de Souza Sampaio Filho
Inaldo de Souza Sampaio Filho
Marlis Elena Ramírez Requelme
Marlis Elena Ramírez Requelme
João Paulo Abreu Almeida
João Paulo Abreu Almeida

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Fabricio Quadros Borges. 2026. “. Global Journal of Human-Social Science – B: Geography, Environmental Science & Disaster Management GJHSS-B Volume 23 (GJHSS Volume 23 Issue B1): .

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Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

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GJHSS-B Classification: DDC Code: 006.3 LCC Code: Q335
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Public Energy Management in Brazil: Decision Analysis and Machine Learning

Fabricio Quadros Borges
Fabricio Quadros Borges
Bruno Alencar da Costa
Bruno Alencar da Costa
Inaldo de Souza Sampaio Filho
Inaldo de Souza Sampaio Filho
Marlis Elena Ramírez Requelme
Marlis Elena Ramírez Requelme
João Paulo Abreu Almeida
João Paulo Abreu Almeida

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