Hierarchical Bayesian propulsion power models for marine vessels

04/15/2020
by   Antti Solonen, et al.
0

Marine traffic is a major contributor to CO2 emissions worldwide. Assessing the magnitude of these emissions on a global scale is a challenging task. However, the emissions can be reduced together with improved cost efficiency by the ways the vessels are operated. Mathematical models for predicting ships' consumption are in a central role in both of these tasks. Nowadays, many ships are equipped with data collection systems, which enable data-based calibration of the consumption models. Typically this calibration procedure is carried out independently for each particular ship, using only data collected from the ship in question. In this paper, we demonstrate a hierarchical Bayesian modeling approach, where we fit a single model over many vessels, with the assumption that the parameters of vessels of same type and similar characteristics (e.g. vessel size) are likely close to each other. The benefits of such an approach are two-fold; 1) we can borrow information about parameters that are not well informed by the vessel-specific data using data from similar ships, and 2) we can use the final hierarchical model to predict the behavior of a vessel from which we don't have any data, based only on its characteristics. In this paper, we discuss the basic concept and present a first simple version of the model. We apply the Stan statistical modeling tool for the model fitting and use real data from 64 cruise ships collected via the widely used commercial Eniram platform. By using Bayesian statistical methods we obtain uncertainties for the model predictions, too. The prediction accuracy of the model is compared to an existing data-free modeling approach.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/19/2023

A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data

Traffic simulation software is used by transportation researchers and en...
research
08/07/2019

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming

Compute and memory constraints have historically prevented traffic simul...
research
04/06/2018

Bayesian Hierarchical Modelling for Tailoring Metric Thresholds

Software is highly contextual. While there are cross-cutting `global' le...
research
12/25/2021

Utilizing gradient approximations to optimize data selection protocols for tumor growth model calibration

The use of mathematical models to make predictions about tumor growth an...
research
07/31/2019

Ice Model Calibration Using Semi-continuous Spatial Data

Rapid changes in Earth's cryosphere caused by human activity can lead to...
research
03/10/2020

Improved VIV response prediction using adaptive parameters and data clustering

Slender marine structures such as deep-water riser systems are continuou...
research
01/20/2021

Bayesian GARCH Modeling of Functional Sports Data

The use of statistical methods in sport analytics has gained a rapidly g...

Please sign up or login with your details

Forgot password? Click here to reset