Asset Details
Do you wish to reserve the book?
A stacking ensemble learning approach for accurate and interpretable prediction of ship energy consumption
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A stacking ensemble learning approach for accurate and interpretable prediction of ship energy consumption
Do you wish to request the book?
A stacking ensemble learning approach for accurate and interpretable prediction of ship energy consumption
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A stacking ensemble learning approach for accurate and interpretable prediction of ship energy consumption
2025
Overview
The accuracy and interpretability of ship energy consumption prediction results are important for ship energy efficiency optimization. In order to improve the accuracy of ship energy consumption prediction and enhance the model interpretability, this paper proposes a ship energy consumption prediction method based on Stacking and SHAP. Firstly, based on Stacking theory, multiple heterogeneous and complementary base models were selected using residual correlation analysis methods to construct a fusion model. And then, to address the “black box” characteristics of the fusion model, SHAP is used to analyze the base model and energy consumption impact characteristics of the fusion model in terms of their interpretability. A large container ship is used as the research object to verify the effectiveness and interpretability of the proposed method. The experimental results show that, in terms of accuracy, compared with the best single model (RF), the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) of the Stacking fusion model are reduced by 4.1%, 16.1%, and 8.3%, respectively, and the R² is improved by 1.5%. Meanwhile, in terms of interpretability, SHAP reveals that Random Forest (RF), k-Nearest Neighbor (KNN), and Gradient Boosting (GB) models play a dominant role in the fusion model, with a total contribution value of about 67%. In addition, sailing speed, mean draft, and trim are the main factors affecting the energy consumption of a ship, and the contribution value of each influential feature can be quantitatively measured. The proposed method ensures the prediction accuracy while enhancing the model interpretability, which can provide more reliable and transparent decision support for ship energy efficiency management.
Publisher
Frontiers Media S.A
