An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction

Fuel consumption prediction (FCP) of vessels is the core of many decarbonization efforts by the maritime industry. Understanding the capability of machine learning (ML) FCP models is essential in various decision-making processes. However, the current model testing practice does not reflect their uncertainty and resilience in actual applications. To address this gap, this study proposes a testing regime that could provide insights into models' behaviors, dependency on different features, and potential vulnerabilities to data uncertainties in the deployment phase. Two multi-ship FCP models were developed for testing, using extreme gradient boosting (XGB) and multi-layer perceptron artificial neural network (ANN) algorithms on noon reports of a container fleet operated globally in 2.5 years. Unlike previous studies, which explicitly indicated the superior ML algorithms, results from this study depicted a complicated situation with no decisive dominance of one algorithm over another, suggesting the potential of model combination and cooperation for optimal application performance. Aiding the FCP model development efforts, this study also includes findings regarding (1) the optimal configurations for ANN models, and (2) the reliance of FCP ML models and algorithms on different fuel consumption influencing factors. To our knowledge, this study is among the first to advocate a more comprehensive understanding of AI-based FCP models' characteristics in realistic scenarios instead of simple selections based on accuracy indicators.