  The maritime industry's continuous commitment to sustainability has led to a
dedicated exploration of methods to reduce vessel fuel consumption. This paper
undertakes this challenge through a machine learning approach, leveraging a
real-world dataset spanning two years of a ferry in west coast Canada. Our
focus centers on the creation of a time series forecasting model given the
dynamic and static states, actions, and disturbances. This model is designed to
predict dynamic states based on the actions provided, subsequently serving as
an evaluative tool to assess the proficiency of the ferry's operation under the
captain's guidance. Additionally, it lays the foundation for future
optimization algorithms, providing valuable feedback on decision-making
processes. To facilitate future studies, our code is available at
\url{https://github.com/pagand/model_optimze_vessel/tree/AAAI}
