  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.
