## 5 Conclusions  
This paper proposed an SA-LSTM model and compared it with ten other models to predict ship fuel consumption and the annual CII value. The self-attention mechanism is introduced to assign weights to the inputs, transforming them into a new matrix, which is fed into the LSTM for model training and prediction. The accuracy of the SA-LSTM model is validated through comparisons with the standard LSTM and traditional models.  
The proposed SA-LSTM could be used to dynamically predict the CII rating of ships, and the ship operators could use this information to dynamically adjust their ship opera  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Annual CII** & **Error** & **Accuracy** & **Rating Grade** \\ \hline XGBoost & 7.7776 & 0.0338 & 99.58\% & A \\ RF & 7.7987 & 0.0128 & 99.84\% & A \\ LGB & 7.8211 & 0.0096 & 99.88\% & A \\ ET & 7.8219 & 0.0104 & 99.87\% & A \\ LASSO & 7.8013 & 0.0102 & 99.87\% & A \\ SVR & 7.7927 & 0.0188 & 99.76\% & A \\ ANN & 7.3837 & 0.4277 & 94.52\% & A \\ ARIMA & 1.3773 & 6.4342 & 17.63\% & A \\ Exponential Smoothing & 1.4739 & 6.3376 & 18.86\% & A \\ LSTM & 7.3723 & 0.4391 & 94.38\% & A \\ SA-LSTM & 7.8130 & 0.0016 & 99.98\% & A \\ True & 7.8115 & 0 & 100\% & A \\ \hline \hline \end{tabular}
\end{table}
Table 4: The annual carbon intensity results of the evaluation by models.  
Figure 11: The fitting degree between the CII evaluated by models and the actual value.  
tional and management practices to enhance the CII ratings of their ships. (1) However, ships need to install the required sensors and their management systems to collect data in advance, which may be expensive and time-consuming. In practice, both data of history and real-time data should be prepared in order to take advantage of this algorithm.  
One limitation of this study is that the proposed SA-LSTM model is simply tested by one case ship, due to the difficulty in collecting real-time fuel consumption data from ships, which may affect the generalization of the results. Additionally, the meteorological and sea condition data were obtained from ECMWF and Copernicus rather than from the ship's sensors, potentially causing a discrepancy in environmental characteristics that could influence model accuracy.  
Future research may focus on improving the generalization of the proposed model by training on data from more ships with different types and sizes. It is also necessary to integrate more real-time operational and environmental datasets and the latest techniques to further improve the performance of the proposed model.  
Conceptualization, Z.W.; methodology, Z.W. and W.L.; software, T.L.; validation, T.L.; formal analysis, Z.W. and T.L.; investigation, Z.W.; resources, Z.W., Y.H.; data curation, T.L.; writing--original draft preparation, T.L.; writing--review and editing, Z.W., T.L., Y.H.; visualization, C.Z.; supervision, Z.W.; project administration, Y.H.; funding acquisition, Z.W., C.Z. and X.Z. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The datasets presented in this article are not readily available because confidentiality of data. Requests to access the datasets should be directed to [PERSON].  
Author [PERSON] was employed by the company COSCO Shipping Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.