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A novel high-precision and self-adaptive prediction method for ship energy consumption based on the multi-model fusion approach

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  • Wang, Kai
  • Liu, Xing
  • Guo, Xin
  • Wang, Jianhang
  • Wang, Zhuang
  • Huang, Lianzhong

Abstract

The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:310:y:2024:i:c:s036054422403041x
    DOI: 10.1016/j.energy.2024.133265
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    References listed on IDEAS

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