Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping
Abstract
The maritime industry is under pressure to increase energy efficiency for climate change mitigation. Navigational data, combining vessel operational and environmental measurements from onboard instruments and external sources, are critical for achieving this goal. Short-sea shipping presents a unique challenge due to the significant influence of surrounding landscape characteristics. With high-resolution onboard data increasingly accessible through IoT devices, appropriate data representations and AI/ML analytical tools are needed for effective decision support. The aim of this study is to investigate the fuel consumption estimation model’s role in developing an energy efficiency decision support tool. ML models that lacking explainability may neglect important factors and essential constraints, such as the need to meet arrival time requirements. Onboard weather measurements are compared to external forecasts, and our findings demonstrate the necessity of eXplainable Artificial Intelligence (XAI) techniques for effective decision support. Real-world data from a short-sea passenger vessel in southern Sweden, consisting of 1754 voyages over 15 months (More of data description and code sources of this study can be found in the GitHub repository at https://github.com/MohamedAbuella/ST4EESSS ), are used to support our conclusions.
Cite
Text
Abuella et al. "Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43430-3_14Markdown
[Abuella et al. "Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/abuella2023ecmlpkdd-datadriven/) doi:10.1007/978-3-031-43430-3_14BibTeX
@inproceedings{abuella2023ecmlpkdd-datadriven,
title = {{Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping}},
author = {Abuella, Mohamed and Atoui, M. Amine and Nowaczyk, Slawomir and Johansson, Simon and Faghani, Ethan},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {226-241},
doi = {10.1007/978-3-031-43430-3_14},
url = {https://mlanthology.org/ecmlpkdd/2023/abuella2023ecmlpkdd-datadriven/}
}