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A machine learning method to evaluate head sea induced weather impact on ship fuel consumption

Author

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  • Zhang, Chi
  • Vergara, Daniel
  • Zhang, Mingyang
  • Nikolaos, Tsoulakos
  • Mao, Wengang

Abstract

A ship's fuel consumption is significantly affected due to ship motions caused by waves and wind when sailing under ocean weather conditions. An essential step to develop certain energy efficiency measures is to understand, model and estimate how much extra fuel consumption is caused by encountering weather conditions, and from which components of a ship's energy system that extra consumption is attributed to. In this study, experimental tests of added resistance in waves during the past decades in open literature are collected and a Gaussian process regression (GPR) model is developed to describe a generic ship's added resistance in head waves. The proposed GPR model achieves better prediction accuracy than semi-empirical formulas (white box) and gives more rational transfer function of added wave resistance coefficient than those produced by the artificial neural networks (ANN), especially in the short-wave regime. The proposed GPR model is integrated into a grey box prediction framework for ship fuel consumption using several years of performance monitoring data collected onboard a chemical tanker. The prediction results indicate an improvement in model performance when moving from the white box to the grey box model, with R2 increasing by 38 % and Root Mean Square Error (RMSE) decreasing by 65 %. Finally, the investigation of weather impact on the ship's extra fuel cost is demonstrated by the proposed model.

Suggested Citation

  • Zhang, Chi & Vergara, Daniel & Zhang, Mingyang & Nikolaos, Tsoulakos & Mao, Wengang, 2025. "A machine learning method to evaluate head sea induced weather impact on ship fuel consumption," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225021759
    DOI: 10.1016/j.energy.2025.136533
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    References listed on IDEAS

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    1. Ruan, Zhang & Huang, Lianzhong & Li, Daize & Ma, Ranqi & Wang, Kai & Zhang, Rui & Zhao, Haoyang & Wu, Jianyi & Li, Xiaowu, 2025. "A novel dual-stage grey-box stacking method for significantly improving the extrapolation performance of ship fuel consumption prediction models," Energy, Elsevier, vol. 318(C).
    2. Karatuğ, Çağlar & Tadros, Mina & Ventura, Manuel & Soares, C. Guedes, 2024. "Decision support system for ship energy efficiency management based on an optimization model," Energy, Elsevier, vol. 292(C).
    3. Luo, Xi & Yan, Ran & Xu, Lang & Wang, Shuaian, 2024. "Accuracy and applicability of ship's fuel consumption prediction models: A comprehensive comparative analysis," Energy, Elsevier, vol. 310(C).
    4. Fan, Ailong & Wang, Yifu & Yang, Liu & Yang, Zhiyong & Hu, Zhihui, 2025. "A novel grey box model for ship fuel consumption prediction adapted to complex navigating conditions," Energy, Elsevier, vol. 315(C).
    5. Elshafei, Basem & Popov, Atanas & Giddings, Donald, 2024. "Enhanced offshore wind resource assessment using hybrid data fusion and numerical models," Energy, Elsevier, vol. 310(C).
    6. Wang, Kai & Liu, Xing & Guo, Xin & Wang, Jianhang & Wang, Zhuang & Huang, Lianzhong, 2024. "A novel high-precision and self-adaptive prediction method for ship energy consumption based on the multi-model fusion approach," Energy, Elsevier, vol. 310(C).
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