Energy Efficient and Safe Ship Routing using Machine Learning Techniques on Operational and Weather Data

This paper presents a method for energy efficient routing of a symmetrical electrical car ferry in Norway. Historical and operational data from the ferry and environmental data (wind, current, and waves) have been used to develop a machine learning model that predicts the energy consumption. Data from more than 2000 trips have been used for training, validation, and testing of the model. By combining weather forecast and the established energy prediction model it is possible to propose more energy efficient route during the transit phase. Energy saving up to 3% are achieved on a selection of representative routes.