The accuracy and interpretability of ship energy consumption prediction play a critical role in energy conservation and emission reduction in the shipping industry. Traditional methods often suffer from issues of low accuracy or acceptability. To address this, an explainable machine learning based ship energy consumption prediction model is proposed. Firstly, sensors were used to collect data related to ship operational energy consumption. Secondly, four different machine learning methods (multiple linear regression (ML), artificial neural networks (ANN), support vector machines (SVM), and extremely randomized trees (ET)) were employed to model ship energy consumption. Finally, the best model was combined with the SHapley Additive exPlanations (SHAP) to perform interpretability analysis on the prediction results. Using an ocean-going container ship as an example, experimental results showed that the ET model improved RMSE and MAPE by 38.08%-69.85% and 47.13%-75.79% compared to other machine learning models. SHAP-based global and local interpretability analysis revealed that ship GPS speed, mean draft, and trim significantly impact energy consumption, with GPS speed being the key factor. The proposed explainable machine learning method not only ensures high accuracy in energy consumption prediction but also provides interpretability, supporting decision-makers in optimizing ship operations and achieving green shipping goals.
Data-Driven Interpretable Machine Learning Methods for the Prediction of Ship Energy Consumption
Lect. Notes Electrical Eng.
International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024
The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation ; Chapter : 48 ; 498-506
2025-03-31
9 pages
Article/Chapter (Book)
Electronic Resource
English
Ship energy consumption prediction method based on data driving
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