Combined machine learning and physics-based models for estimating fuel consumption of cargo ships

Abstract

The International Maritime Organization (IMO) has enforced stricter limits on the Greenhouse Gas (GHG) emission due to environment and climate concerns. Many measures have emerged to reduce GHG emission from shipping. However, the effectiveness and applicability of any measure depend on ship type, ship size, operational mode and the operating ocean environment. Estimation of ship fuel consumption and emission in actual shipping scenarios are fundamental input to evaluate the impacts of shipping on the environment and climate, and to evaluate the effect of new measures to reduce GHG emission. A model for estimating ship fuel consumption and emission, based on ship hydrodynamical models and machine learning techniques, utilizing information from Automatic Identification System (AIS), the ship information database and metocean data is presented in this paper. The model is able to estimate the ship fuel consumption at high computational speed by utilizing machine learning (ML) technology, to evaluate the effect of new measures to reduce GHG emission. The power consumption calculated by the combined model is compared with measurement data from two container ships and two bulk carriers for validation. This validation shows that the model can predict ship power consumption well.