Multi-Horizon Prediction of Power Consumption during Dynamic Positioning Operations: Physical, Data-Driven and Hybrid Model Approaches

A significant decrease in greenhouse gas emissions can be achieved by including a prediction of future power consumption in the control of ships’ power plants during transits. Moreover, including a prediction of power consumption in the Energy Management System (EMS) during Dynamic Positioning (DP) operations can also contribute to a reduction...

Adaptive Learning of Inland Ship Power Propulsion under Environmental Disturbances

This paper presents an adaptive approximation-based scheme for learning a partially known ship power propulsion plant under various environmental conditions. Considering the effect of water depth on the engine power, a dynamic model is defined comprised of the engine dynamics and the 1-DoF ship manoeuvring dynamics. The modelling challenge is...

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction...

Analysis of a Hybrid RaNS-BEM Method for Predicting Ship Power

Predicting the equilibrium between a ship’s required thrust and resistance is a complicated problem. Shipyards have to pay big penalties when a ship does not run at its design speed at a specified required power. An accurate prediction of the ship’s self-propulsion point is thus of huge importance. In the determination of the self-propulsion...