Data Analytics for Improving Energy Efficiency in Short Sea Shipping

TL;DR

This paper presents a data-driven approach using explainable AI to model and optimize energy efficiency in short-sea shipping vessels, utilizing onboard data and spatial clustering for route analysis.

Contribution

It introduces a novel framework combining data aggregation, explainable AI, and route clustering to improve vessel energy efficiency and voyage optimization in short-sea shipping.

Findings

Effective vessel energy modeling using onboard data

Optimized voyage planning reduces fuel consumption

Spatial clustering identifies repeatable vessel routes

Abstract

To meet the urgent requirements for the climate change mitigation, several proactive measures of energy efficiency have been implemented in maritime industry. Many of these practices depend highly on the onboard data of vessel's operation and environmental conditions. In this paper, a high resolution onboard data from passenger vessels in short-sea shipping (SSS) have been collected and preprocessed. We first investigated the available data to deploy it effectively to model the physics of the vessel, and hence the vessel performance. Since in SSS, the weather measurements and forecasts might have not been in temporal and spatial resolutions that accurately representing the actual environmental conditions. Then, We proposed a data-driven modeling approach for vessel energy efficiency. This approach addresses the challenges of data representation and energy modeling by combining and aggregating data from multiple sources and seamlessly integrates explainable artificial intelligence (XAI) to attain clear insights about the energy efficiency for a vessel in SSS. After that, the developed model of energy efficiency has been utilized in developing a framework for optimizing the vessel voyage to minimize the fuel consumption and meeting the constraint of arrival time. Moreover, we developed a spatial clustering approach for labeling the vessel paths to detect the paths for vessels with operating routes of repeatable and semi-repeatable paths.