Moreira, L., Vettor, R. and Guedes Soares, C. (2021), “Neural network approach for predicting ship speed and fuel consumption”, Journal of Marine Science and Engineering, Vol. 9, 119 (15 pages).

Simulations of a ship travelling a given oceanic route, are performed by a weather routing system, to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation. This data set is employed to train a Neural Network computing system in order to predict the ship speed and fuel consumption. The model is trained using the Levenberg-Marquardt backpropagation scheme to establish the relation between the ship speed and the respective propulsion configuration for the existing the sea conditions, i.e, the output torque of the main engine, the revolutions per minute of the propulsion shaft, the significant wave height, the peak period of the waves together with the relative angle of wave encounter. Additional results are obtained using the model also to train the relationship between the same inputs used to determine the speed of the ship and the fuel consumption. A sensitivity analysis is performed to analyze the Artificial Neural Network capability to forecast the ship speed and fuel oil consumption without information of the status of the engine (the revolutions per minute and torque) using as inputs only the information of the sea state. The results obtained with the neural network model show very good accuracy both in the prediction of the speed of the vessel and the fuel consumption.

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