Assessment of ship powering performance using machine learning techniques

Measuring changes in ship fuel consumption caused by specific energy-saving technologies is difficult, particularly for data acquired in a sea-state and where the sea-state itself is not separately recorded. It is also challenging to predict the behaviour of similar vessels in a fleet, where data is not measured. This limits the ability of shipping companies to monitor emissions, inhibits introduction of new technologies and reduces scope for optimisation of fleet operations.This project will develop and apply soft computing and machine-learning techniques to develop robust and reliable models of ship powering performance. Such models must be accurate enough to detect small changes in performance arising from use of, say, energy-saving devices and to have predictive capability for similar vessels from one (or a few) instrumented vessels across a fleet. It is intended that a machine learning approach be applied to model ship performance both over time and in different operational conditions.