An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels

Yiannis Kiouvrekis (), Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou and Ioannis Filippopoulos
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Yiannis Kiouvrekis: Mathematics, Computer Science and Artificial Intelligence Lab, Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece
Katerina Gkirtzou: Institute for Language and Speech Processing, Athena Research Center, 15125 Athens, Greece
Sotiris Zikas: Mathematics, Computer Science and Artificial Intelligence Lab, Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece
Dimitris Kalatzis: Mathematics, Computer Science and Artificial Intelligence Lab, Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece
Theodor Panagiotakopoulos: Department of Management Science and Technology, University of Patras, 26334 Patras, Greece
Zoran Lajic: Angelicoussis Group, 17674 Athens, Greece
Dimitris Papathanasiou: Angelicoussis Group, 17674 Athens, Greece
Ioannis Filippopoulos: Shipping Operations and Computer Science, University of Limassol, Limassol 3086, Cyprus

Future Internet, 2025, vol. 17, issue 6, 1-24

Abstract: In the evolving landscape of green shipping, the accurate estimation of shaft power is critical for reducing fuel consumption and greenhouse gas emissions. This study presents an explainable machine learning framework for shaft power prediction, utilising real-world Internet of Things (IoT) sensor data collected from nine (9) Very Large Crude Carriers (VLCCs) over a 36-month period. A diverse set of models—ranging from traditional algorithms such as Decision Trees and Support Vector Machines to advanced ensemble methods like XGBoost and LightGBM—were developed and evaluated. Model performance was assessed using the coefficient of determination ( R 2 ) and RMSE, with XGBoost achieving the highest accuracy ( R 2 = 0.9490 , RMSE 888) and LightGBM being close behind ( R 2 = 0.9474 , RMSE 902), with both substantially exceeding the industry baseline model ( R 2 = 0.9028 , RMSE 1500). Explainability was integrated through SHapley Additive exPlanations (SHAP), offering detailed insights into the influence of each input variable. Features such as draft, GPS speed, and time since last dry dock consistently emerged as key predictors. The results demonstrate the robustness and interpretability of tree-based methods, offering a data-driven alternative to traditional performance estimation techniques and supporting the maritime industry’s transition toward more efficient and sustainable operations.

Keywords: explainable artificial intelligence; shaft power estimation; machine learning; vessel performance monitoring; maritime data analytics; fuel efficiency (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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