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Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping
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- Abuella, Mohamed, 1980- (author)
- Högskolan i Halmstad,Centrum för forskning om tillämpade intelligenta system (CAISR)
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- Atoui, M. Amine, 1989- (author)
- Högskolan i Halmstad,Centrum för forskning om tillämpade intelligenta system (CAISR)
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- Nowaczyk, Sławomir, 1978- (author)
- Högskolan i Halmstad,Centrum för forskning om tillämpade intelligenta system (CAISR)
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- Cham : Springer, 2023
- 2023
- English.
- In: Machine Learning and Knowledge Discovery in Databases. - Cham : Springer. - 9783031434297 - 9783031434303 ; , s. 226-241
- Conference paper (peer-reviewed)
Abstract
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- Short-sea shipping
- Energy efficiency
- Explainability
- Spatio-temporal aggregation
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