This work presents a case study of the application of machine learning techniques to model the load of the main equipments of three of Petrobras' FPSO units. The methodology proposed was used in the development of a modelling and simulation tool called FPSO Power Demand Analytics (FPDA), developed in a partnership between Universidade Federal Fluminense (UFF) and Petrobras. The applied 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%. The presented results were found satisfactory by UFF and Petrobras' development and engineering teams.