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A stacking ensemble learning for ship fuel consumption prediction under cross-training

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Abstract

Accurate ship fuel consumption prediction is vital for the shipping industry. In this study, a stacking ensemble learning is developed to predict tanker fuel consumption precisely, built on cross-training of the first-level learner. Among comparative experiments, stacking with Bayesian regression (BR) as the meta-learner and extremely randomized trees (ET), gradient boosting decision tree (GBDT) and light gradient boosting machine (LGBM) as firstlevel learners achieves superior performance, yielding the best results. The root mean square error (RMSE) on the test dataset is 0.2679, and on the training dataset is 0.1327. Ensemble model-based feature importance analysis reveals that ship attributes (speed, draught, trim) contribute around 80 %, while meteorological features contribute about 20 %.

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Abbreviations

BR :

Bayesian regression

ET :

Extremely randomized trees

GBDT :

Gradient boosting decision tree

LGBM :

Light gradient boosting machine

RMSE :

Root mean square error

IMO :

International Maritime Organization

ML :

Machine learning

MLR :

Multiple linear regression

RR :

Ridge regression

ANN :

Artificial neural network

SVM :

Support vector machine

DT :

Decision tree

KNN :

K-nearest neighbor

RF :

Random forest

AB :

Adaptive boosting

TPE :

Tree-structured parzen

XGB :

Extreme gradient boosting

R 2 :

R square

EI :

Expected incremental

GC :

Golden creation

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Acknowledgments

This work is supported by the National Social Science Fund (21BGJ073) of China.

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Authors and Affiliations

  1. College of Transportation Engineering, Dalian Maritime University, Dalian, Liaoning, China

    Mengjie Ma, Zhuo Sun, PeiXiu Han & Huirong Yang

Authors
  1. Mengjie Ma
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  2. Zhuo Sun
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  3. PeiXiu Han
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  4. Huirong Yang
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Corresponding author

Correspondence to Zhuo Sun.

Additional information

Mengjie Ma is a graduate student of the College of Transportation Engineering, Dalian Maritime University, Dalian, Liaoning, China. His research interests include green and intelligent transportation, machine learning, data mining and operational research.

Zhuo Sun is a Professor at the Department of Logistics in Dalian Maritime University. His research interests are in the areas of shipping network design and port planning. He developed a spatial planning tool named MicroCity (https://microcity.github.io).

Peixiu Han is a doctoral student of the College of Transportation Engineering, Dalian Maritime University, Dalian, Liaoning, China. Her research interests include green and intelligent transportation, machine learning, data mining and operational research.

Huirong Yang is a graduate student of the College of Transportation Engineering, Dalian Maritime University, Dalian, Liaoning, China. Her research interests include green and intelligent transportation, machine learning, data mining and operational research.

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Ma, M., Sun, Z., Han, P. et al. A stacking ensemble learning for ship fuel consumption prediction under cross-training. J Mech Sci Technol 38, 299–308 (2024). https://doi.org/10.1007/s12206-023-1224-9

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  • DOI: https://doi.org/10.1007/s12206-023-1224-9

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