Predictive analysis of fuel oil consumption in vessels: interpretable modelling with emphasis on load conditions

Fuel oil consumption (FOC) in vessels is influenced by various factors, with vessel load conditions being a critical determinant. This research aimed to develop an interpretable regression-based machine learning model, specifically an XGBoost Regressor, to predict FOC, and leveraged the analysis with Explainable Artificial Intelligence (XAI) techniques to enhance transparency and understanding of the factors affecting FOC in maritime operations. Hyperparameter tuning optimized model parameters, achieving a high R-squared (R2) value of 0.99. XAI methods clarified how operational (e.g., speed, load, draft) and environmental factors (e.g., wind, wave, current, sea state) contribute to FOC increases. As a result, operational factors, notably average draft, exerted a substantial influence, with an average draft of 18.425 m resulting in a significant increase in predicted FOC to 1,729.40 kg/h during the laden condition, compared to the base value of 1,496.82 kg/h. Additionally, environmental factors, particularly Relative Wind Angle, significantly impacted FOC prediction, with a Mean Absolute SHAP Value of 11.97 kg/h, notably higher than in ballast (2.59 kg/h) and empty (2.85 kg/h) conditions. These findings emphasize the importance of tailored fuel efficiency strategies, as operational and environmental factors may vary in their impact across different loading conditions. ‘This article is a revised and expanded version of a paper entitled “Predictive Analysis of Fuel Oil Consumption in Vessels: Interpretable Modeling with Emphasis on Load Conditions” presented at The 11th International Conference on Logistics and Maritime Systems (LOGMS 2023) on 6 September 2023 at the Busan Port International Exhibition & Convention Center (BPEX)’