## 4 Conclusions  
This study demonstrates the efficacy of integrating machine learning and explainable AI techniques in the domain of shaft power estimation for large marine vessels. Using a comprehensive dataset from VLCCs spanning three years of operation, we developed predictive models capable of accurately estimating shaft power under varying operational and environmental conditions. Among the models evaluated, XGBoost emerged as the best-performing algorithm, achieving \(R^{2}=0.9490\) on the validation set, followed by LightGBM with \(R^{2}=0.9474\). Both ensemble methods outperformed the traditional industry baseline model (\(R^{2}=0.9028\)), corresponding to relative improvements of approximately 5.1% and 4.8%, respectively. To enhance transparency, we incorporated SHAP (SHapley Additive exPlanations) into our modelling pipeline. This approach allowed us to quantify and visualise the contribution of each input variable to the final prediction, addressing one of the major criticisms of black-box machine learning models--lack of interpretability. Variables such as draft, GPS speed, and maintenance-related metrics (e.g., time since dry dock) consistently ranked among the most influential features, offering domain-relevant insights that align with physical expectations and marine engineering principles. Compared to traditional estimation techniques--often based on sea trial curves and empirically derived correction factors--the proposed data-driven approach offers superior adaptability, accuracy, and generalisation. Traditional models tend to be rigid and assume constant operating conditions, whereas our models account for the full spectrum of real-world variation, including weather effects, operational profiles, and degradation over time. The inclusion of the RMSE in our evaluation provides a practical measure of the model's prediction error in physical units. For example, the XGBoost model achieved an RMSE of 888.30\(\sim\)kW, which--given typical fuel consumption rates of 150-300\(\sim\)g/kWh--translates to an uncertainty of roughly 130-210\(\sim\)kg of fuel per hour. This level of accuracy supports use in operational settings, where reliable shaft power estimates are essential for fuel efficiency monitoring, emission tracking, and voyage optimisation. Moreover, the high \(R^{2}\) values across models confirm the soundness and completeness of the dataset, indicating that the model effectively captures the core dynamics of marine propulsion. Furthermore, our findings suggest that machine learning can serve not only as a performance estimation tool but also as a decision support system for operational optimisation, fleet management, and regulatory compliance. By integrating explainability into the modelling framework, we build user trust and open avenues for practical deployment on board ships and within shore-based monitoring systems. The proposed models can support voyage optimisation by predicting shaft power under varying weather and loading conditions, allowing route planners to minimise fuel consumption. Additionally, the insights provided by SHAP can inform maintenance decisions, such as scheduling hull cleaning based on performance degradation patterns. Future work could extend this framework to include emission prediction, optimisation of voyage planning, and real-time integration with weather forecasts. Another promising direction lies in the application of federated learning across fleets, enabling data privacy while using distributed intelligence. As the maritime industry continues its digital transformation, explainable machine learning offers a powerful tool for achieving environmental, economic, and operational goals. **Author Contributions:** Conceptualisation, Y.K., T.P. and I.F. ; methodology, S.Z., K.G., D.K. and Y.K. ; software, S.Z., K.G. and D.K. ; validation, S.Z., K.G., Z.L., D.P., I.F. and Y.K. ; formal analysis, Y.K. ; investigation, Z.L., D.P., I.F. and Y.K. ; resources, Z.L., D.P. and I.F. ; data curation, S.Z., K.G., Z.L., D.P., I.F. and Y.K. ; writing--original draft preparation, S.Z.