Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)
This work addresses fuel efficiency for the maritime industry, but it is incremental as it applies existing methods to a new dataset without claiming major breakthroughs.
The paper tackled reducing vessel fuel consumption by developing a time series forecasting model using a two-year dataset from a ferry in Canada, which predicts dynamic states to evaluate the captain's operational proficiency and support future optimization.
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}