Data-Driven Ship Propulsion Modeling with Artificial Neural Networks

The paper examines data-driven techniques for the modeling of ship propulsion that could support a strategy for the reduction of emissions and be utilized for the optimization of a fleet’s operations. A large, high-frequency and automated collected data set is exploited for producing models that estimate the required shaft power or main engine’s fuel consumption of a container ship sailing under arbitrary conditions. A variety of statistical calculations and algorithms for data processing are implemented and state-of-the-art techniques for training and optimizing Feed-Forward Neural Networks (FNNs) are applied. Emphasis is given in the pre-processing of the data and the results indicate that with a proper filtering and preparation stage it is possible to significantly increase the model’s accuracy. Thus, increase our prediction ability and our awareness regarding the ship's hull and propeller actual condition.