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
https://github.com/pagand/model_optimze_vessel/tree/AAAI

用机器学习方法，通过两年的加拿大西海岸渡轮真实数据集，针对动态和静态状态、行动和干扰，创建了一个时间序列预测模型，用于预测动态状态，为评估渡轮在船长指导下的操作效能提供工具，并为未来的优化算法打下基础，并提供了决策过程的有价值的反馈。