Public Energy Management in Brazil: Decision Analysis and Machine Learning

Authors

  • Fabricio Quadros Borges

  • Bruno Alencar da Costa

  • Inaldo de Souza Sampaio Filho

  • Marlis Elena Ramírez Requelme

  • João Paulo Abreu Almeida

DOI:

https://doi.org/10.34257/GJHSSBVOL23IS1PG1

Keywords:

planning; sustainability; electricity

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

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How to Cite

Fabricio Quadros Borges, Bruno Alencar da Costa, Inaldo de Souza Sampaio Filho, Marlis Elena Ramírez Requelme, & João Paulo Abreu Almeida. (2023). Public Energy Management in Brazil: Decision Analysis and Machine Learning. Global Journal of Human-Social Science, 23(B1), 1–11. https://doi.org/10.34257/GJHSSBVOL23IS1PG1

Public Energy Management in Brazil: Decision Analysis and Machine Learning

Published

2023-02-24