Benchmark Study of Supervised Machine Learning Methods for a Ship Speed-Power Prediction at Sea

The development and evaluation of energy efficiency measures to reduce air emissions from shipping strongly depends on reliable description of a ship’s performance when sailing at sea. Normally, model tests and semi-empirical formulas are used to model a ship’s performance but they are either expensive or lack accuracy. Nowadays, a lot of ship performance-related parameters have been recorded during a ship’s sailing, and different data driven machine learning methods have been applied for the ship speed-power modelling. This paper compares different supervised machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), neural network, support vector machine, and some statistical regression methods, for the ship speed-power modelling. A worldwide sailing chemical tanker with full-scale measurements is employed as the case study vessel. A general data pre-processing method for the machine learning is presented. The machine learning models are trained using measurement data including ship operation profiles and encountered metocean conditions. Through the benchmark study, the pros and cons of different machine learning methods for the ship’s speed-power performance modelling are identified. The accuracy of various algorithms based models for ship performance during individual voyages is also investigated.