An Artificial Neural Network Based Decision Support System for Cargo Vessel Operations

Abstract

There is increasing interest in understanding fuel consumption from the perspective of increasing energy efficiency on a vessel. Thus the aim of this paper is to present a new framework for data-driven estimation of fuel consumption by employing a combination of (i) traditional statistical analysis and (ii) Artificial Neural Networks. The output of the analysis is the most frequently occurring fuel-speed curves corresponding to the respective operational profile. The inputs to the model consider important explanatory variables like draft, sea current and wind. The methodology is applied to a case study of a fleet of 9000 TEU vessels, in which telemetry data on the fuel consumption, vessel speed, current, wind direction and strength were analysed. The performance of the method is validated in terms of error estimation criterion like R 2 values and against physical phenomena obtained from the data. The results can be used to study the economic and environmental benefits of slow-steaming and or fuel levies, or by extending this part of the model into exergy analysis for a more holistic review of energy saving initiatives.

Self-labelling of tugboat operation using unsupervised machine learning and intensity indicator

“…Scikit-learn is an open-source software suite for data analytics as well as machine learning (clustering). Tensorflow machine learning toolkit is used to construct, train, and predict a neural network (Jain et al, 1996;Fam et al, 2022Fam et al, , 2021, as a sample application for the solution presented in this paper.…”