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Gabriel de Campos
Instituto de Engenharia de Sistemas e Tecnologia da Informação, Universidade Federal de Itajubá, Itajubá-MG
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Mateus G. Santos
Instituto de Engenharia de Sistemas e Tecnologia da Informação, Universidade Federal de Itajubá, Itajubá-MG
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Pedro Paulo C. Viana
Jirau Energia, Porto Velho-RO
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Marcelo O. Fonseca
Jirau Energia, Porto Velho-RO
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Guilherme S. Bastos
Instituto de Engenharia de Sistemas e Tecnologia da Informação, Universidade Federal de Itajubá, Itajubá-MG
Keywords:
Principal component analysis, Data reduction, Hydropower, Artificial intelligence, systems optimization
Abstract
The increased amount of monitored data allows more complex analysis and optimization techniques. As a result of the high dimensional databases, the computational cost has become a critical point. This paper proposes the application of the Principal Component Analysis (P.C.A.) to reduce high dimensional databases, applied to a hydroelectrical generation dataset, and evaluate its impacts on supervised machine learning techniques prediction. The findings of this study showed that by applying the P.C.A. the reduced dataset was able to preserve most of the data variability of original data and achieved a similar performance rate on machine learning techniques, compared to the non-reduced dataset.