Towards Data-Driven Fuel Consumption Model Transferability Between Sister Vessels: A Case Study Using Tuna Purse Seiners

Abstract

Fuel Oil Consumption (FOC) represents a significant portion of a fishing vessel’s operating costs, often exceeding 50%. Accurately forecasting FOC during the voyage planning stage is crucial but challenging for optimizing routes and supporting decision-making systems aimed at fuel-saving. Data-driven models have shown excellent performance in FOC prediction. However, gathering the necessary data for these models is expensive and time-consuming. Even though, the applicability of FOC model derived from one vessel to predict FOC for another vessel has received limited research attention. This paper investigates the performance in predicting FOC for an unseen tuna purse seiner, using a two-stage model trained on metocean and operational data, from Copernicus and sensors installed on her similar vessel, respectively. By considering the engine performance modifications, the two-stage model trained on the similar vessel achieves high mean accuracies (over 94%) in predicting FOC for the unseen vessel.