[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125670-en":3,"doc-seo-125670-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125670,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Public Energy Management in Brazil - Decision Analysis and Machine Learning - Research","Artificial intelligence analyses require a complete, integrated data structure to classify and group information for synergistic mental and predictive insights. This study evaluates AI’s potential to support decision-making in public planning of sustainable electrical power matrices in Brazil. It develops an analytical framework for electricity sources across economic, social, environmental, and technological dimensions, plus sectoral sustainability indicators supported by linear correlations. Results indicate AI can guide strategic decisions when grounded in structured, multidimensional indicator analysis.","Global Journal of HUMAN-SOCIAL SCIENCE: B  \nGeography, Geo-Sciences, Environmental Science & Disaster Management  \nVolume 23 Issue 1 Version 1.0 Year 2023  \nType: Double Blind Peer Reviewed International Research Journal  \nPublisher: Global Journals  \nOnline ISSN: 2249-460x & Print ISSN: 0975-587X  \nPublic Energy Management in Brazil: Decision Analysis and Machine Learning  \nBy Fabricio Quadros Borges, Bruno Alencar da Costa, Inaldo de Sousa Sampaio Filho, Marlis Elena Ramírez Requelme & João Paulo Abreu Almeida  \nUniversity of the Amazon  \nAbstract-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.  \nKeywords: planning; sustainability; electricity.  \nGJHSS-B Classification: DDC Code: 006.3 LCC Code: Q335  \nPublicEnergyManagementinBrazilDecisionAnalysisandMachineLearning  \nStrictly as per the compliance and regulations of:  \nPublic Energy Management in Brazil: Decision Analysis and Machine Learning  \nFabricio Quadros Borges α , Bruno Alencar da Costa σ , Inaldo de Sousa Sampaio Filho ρ , Marlis Elena Ramírez Requelme Ѡ & João Paulo Abreu Almeida¥  \nAbstract-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.  \nKeywords: planning; sustainability; electricity.  \nI. Introduction  \nStudies on A.I. emerged in the 1950s, through  \nscientists Herbert Simon and Allen Newell, who created the first artificial intelligence laboratory at Carn","cbCair9GpKoEGRTT","https://ap.wps.com/l/cbCair9GpKoEGRTT","pdf",613001,1,12,"English","en",105,"# Introduction\n## Artificial Intelligence Background and Relevance\n## Objectives and Planning Context","[{\"question\":\"What is the study’s main objective?\",\"answer\":\"To analyze how artificial intelligence can contribute to decision-making in public planning for sustainable electrical power matrices.\"},{\"question\":\"How does the methodology structure the analysis?\",\"answer\":\"It builds a framework for electricity sources based on economic, social, environmental, and technological dimensions, and it structures sustainability indicators with linear correlations.\"},{\"question\":\"Why is AI considered strategic for power planning?\",\"answer\":\"It can act as a strategic guide for decisions when supported by structured analysis focused on electricity source use and sectoral, multidimensional indicators.\"}]","Public Energy Management in Brazil - 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