LAUSR

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.