LEITE, Jandecy Cabral2025-01-312024KAMIO, Edson; GUIMARÃES, Gil Eduardo; LEITE, Jandecy Cabral; ALMEIDA, Anderson Alexandre Silva de; MONTEIRO, Odilon Bentes; RIBEIRO, Paulo Francisco da Silva; NASCIMENTO FILHO, Alarico Gonçalves do. Arquitetura computacional para controle de monitoramento de qualidade de energia utilizando ferramentas de inteligência artificial focado na Indústria 4.0 com integração de energias renováveis. Revista de Gestão e Secretariado – GeSec, São José dos Pinhais, PR, v. XX, n. X, p. XX-XX, 202X. DOI: http://doi.org/10.7769/gesec.vXXiX.https://rigalileo.itegam.org.br/handle/123456789/315The quality of electrical energy (QEE) is crucial to the efficiency of industrial systems, especially in Industry 4.0. This research proposes a computer architecture for QEE control and monitoring, integrating renewable sources and artificial intelligence (AI). The proposal uses AI for predictive monitoring and dynamic control of capacitor banks and harmonic filters, mitigating issues such as inadequate power factor and high total harmonic distortion. Additionally, the system strategically integrates solar energy to maximize cost savings and sustainability in the industrial environment. An innovative software solution, utilizing the IMS Smart Cap 485 device via the Modbus-RTU protocol, collects and analyzes electrical data, processing it with deep learning (LSTM) and optimization (GA) algorithms. Interactive dashboards developed in Python provide detailed visualizations to predict problems and make strategic decisions, optimizing energy efficiency and reducing dependence on conventional sources.pdf.Indústria 4.0Qualidade de Energia Elétrica (QEE)Fator de Potência (FP)Distorção Harmônica Total (THD)Inteligência Artificial (IA)ArtigoEngenharia Elétrica