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dc.contributor.advisorOrientação
dc.contributor.authorUrzagasti, Carlos Alejandro
dc.date.accessioned2022-08-09T15:42:16Z
dc.date.available2022-08-09T15:42:16Z
dc.date.issued2022
dc.identifier.urihttp://dspace.unila.edu.br/123456789/6706
dc.descriptionTrabalho de Conclusão de Curso apresentado ao Instituto Latino-Americano de Tecnologia, Infraestrutura e Território da Universidade Federal da Integração Latino-Americana, como requisito parcial à obtenção do título de Bacharel em Engenharia de Energia.pt_BR
dc.description.abstractOne of the most important research topics in Smart Grid technology is load curve prediction. This allows electric power transmission and distribution operators to carry out operations with greater efficiency in the dispatch of electric power. Therefore, the relevance of knowing the characteristics of the electricity consumption profiles of consumers is highlighted, in order to then make load predictions, which can be done in different prediction horizons and with different methods. Therefore, this study aims to present machine learning techniques to perform load predictions within 30 minutes, ANN and LTSM techniques are analyzed. From a database containing load curve information of a 4-year distribution system (2013 to 2016), 4 machine learning models were made, two from RNA and two from LSTM. A comparison of both models was made, where a better performance was found for the LSTM models with accuracy greater than 0.95. It should be noted that this work only made a comparison for a single forecast horizon, therefore it remains as future work to analyze for different forecast horizons and search for new open databases to complement this study.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccess
dc.subjectSistemas elétricos de potência
dc.subjectModelos de aprendizado de máquina
dc.subjectMétodos de MLP e LSTM
dc.titleAplicação de Modelos de Aprendizado de Máquina na Predição de Curvas de Cargapt_BR
dc.typebachelorThesispt_BR


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