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.

翻译：为应对减缓气候变化的迫切需求，海事行业已实施多项主动性能效措施。这些实践大多高度依赖船舶运行及环境条件的船载数据。本文采集并预处理了短途海运（SSS）客船的高分辨率船载数据。首先通过调查可用数据，有效部署以模拟船舶物理特性及其性能表现。鉴于短途海运中，气象测量与预报的时空分辨率可能无法精确表征实际环境条件，我们提出了一种面向船舶能效的数据驱动建模方法。该方法通过多源数据融合与聚合，解决数据表征与能量建模难题，并无缝集成可解释人工智能（XAI）以获取关于短途海运船舶能效的清晰洞察。随后，基于所开发的能效模型构建了船舶航程优化框架，在满足到达时间约束的前提下最小化燃油消耗。此外，我们提出了空间聚类方法对船舶航迹进行标定，用于识别具有重复性及半重复性航行路线的船舶路径。