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.
Data-Driven Ship Propulsion Modeling with Artificial Neural Networks
Karagiannidis, Pavlos, and Nikolaos Themelis. "Data-Driven Ship Propulsion Modeling with Artificial Neural Networks." Paper presented at the SNAME 7th International Symposium on Ship Operations, Management and Economics, Virtual, April 2021. doi: https://doi.org/10.5957/SOME-2021-011
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