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Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7796-5201
CetaSol AB, Gothenburg, Sweden.
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2023 (English)In: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII / [ed] Gianmarco De Francisci Morales; Claudia Perlich; Natali Ruchansky; Nicolas Kourtellis; Elena Baralis; Francesco Bonchi, Cham: Springer, 2023, Vol. 14175, p. 226-241Conference paper, Published paper (Refereed)
Abstract [en]

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.  © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Cham: Springer, 2023. Vol. 14175, p. 226-241
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14175
Keywords [en]
Short-sea shipping, Energy efficiency, Explainability, Spatio-temporal aggregation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-51888DOI: 10.1007/978-3-031-43430-3_14Scopus ID: 2-s2.0-85174447269ISBN: 978-3-031-43429-7 (print)ISBN: 978-3-031-43430-3 (electronic)OAI: oai:DiVA.org:hh-51888DiVA, id: diva2:1809072
Conference
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023
Projects
Research projects within Aware Intelligent Systems
Funder
VinnovaAvailable from: 2023-11-02 Created: 2023-11-02 Last updated: 2023-12-07Bibliographically approved

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Atoui, M. AmineNowaczyk, Sławomir

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