Ocean Engineering | 2021
Data-driven modelling of ship propulsion and the effect of data pre-processing on the prediction of ship fuel consumption and speed loss
Abstract
Abstract Data-driven models for ship propulsion are presented while the effect of data pre-processing techniques is extensively examined. In this study, a large, automatically collected with high sampling frequency data set is exploited for training models that estimate the required shaft power or main engine fuel consumption of a container ship sailing under arbitrary conditions. Emphasis is given to the statistical evaluation and pre-processing of the data and two algorithms are presented for this scope. Additionally, state-of-the-art techniques for training and optimizing Feed-Forward Neural Networks (FNNs) are applied. The results indicate that with a delicate filtering and preparation stage it is possible to significantly increase the model s accuracy. Therefore, increase the prediction ability and awareness regarding the ship s hull and propeller actual condition. Furthermore, such models could be employed in studies targeting at the improvement of ship s operational energy efficiency.