Achieving fuel efficiency of harbour craft vessel via combined time-series and classification machine learning model with operational data

This paper presents work on forecasting the fuel consumption rate of a harbour craft vessel through the combined time-series and classification prediction modelling. This study utilizes the machine learning tool which is trained using the 5-month raw operational data, i.e., fuel rate, vessel position and wind data. The Haar wavelet transform filters the noisy readings in the fuel flow rate data. Wind data are transformed into wind effect (drag), and the vessel speed is acquired through transforming GPS coordinates of vessel location to vessel distance travelled over time. Subsequently, the k-means clustering groups the tugboat operational data from the same operations (i.e., cruising and towing) for the training of the classification model. Both the time-series (LSTM network) and classification models are executed in parallel to make prediction results. The comparison of empirical results is made to discuss the effect of different architectures and hyperparameters on the prediction performance. Finally, fuel usage optimization by hypothetical adjustment of vessel speed is presented as one direct application of the methods presented in this paper.