Título: Unsupervised and Supervised Techniques for FPSO Electric Power Demand Modelling
Autores: Daniel de Araujo, Vitor Ferreira, Artur Pessoa, Marcio Fortes, Bruno Borba, Andre Augusto, Andre Pinho, Angelo Colombini, Marcos Ramos, Gabriel Mafra
Resumo: This work presents a case study of the application of supervised and unsupervised machine learning techniques in the study of three of Petrobras FPSO units in regards to its equipments power demand. Specifically, it delves into the outcomes of the clustering and equipment modelling modules of a computational solution developed in a partnership between Universidade Federal Fluminense (UFF) and Petrobras, FPSO Power Demand Analytics (FPDA). The presented results were found satisfactory by UFF and Petrobras development and engineering teams. For example the equipment modelling methodology resulted in a library of models from which the median absolute error rarely exceeds the 3% mark. The median of the median absolute errors observed across platforms and test scenarios is often less than 1%.
Palavras-chave: FPSO, Machine Learning, Modelling, Neural Network, Artificial Intelligence, Clustering
Páginas: 6
Código DOI: 10.21528/CBIC2023-018
Artigo em pdf: CBIC_2023_paper018.pdf
Arquivo BibTeX: CBIC_2023_018.bib