Prediction of Ship CO 2 Emissions and Fuel Consumption Using Voting-BRL Model

The accurate prediction of ship carbon dioxide (CO 2 ) emissions and fuel consumption is critical for enhancing environmental sustainability in the maritime industry. This study introduces a novel ensemble learning approach, the Voting-BRL model, which integrates Bayesian Ridge Regression and Lasso Regression to improve prediction accuracy and robustness. Utilizing four years of real-world data from the THETIS-MRV platform managed by the European Maritime Safety Agency (EMSA), the proposed model first employs Analysis of Variance (ANOVA) for feature selection, effectively reducing dimensionality and mitigating noise interference. The Voting-BRL model then combines the strengths of Bayesian Ridge Regression in handling uncertainty and feature correlations with Lasso Regression’s capability for automatic feature selection through a voting mechanism. Experimental results demonstrate that Voting-BRL achieves an R 2 of 0.9981 and a Root Mean Square Error (RMSE) of 8.53, outperforming traditional machine learning models such as XGBRegressor, which attains an R 2 of 0.97 and an RMSE of 45.03. Additionally, ablation studies confirm that the ensemble approach significantly enhances predictive performance by leveraging the complementary strengths of individual models. The Voting-BRL model not only provides superior accuracy but also exhibits enhanced generalization capabilities and stability, making it a reliable tool for predicting ship CO 2 emissions and fuel consumption. This advancement contributes to more effective emission management and operational efficiency in the shipping sector, supporting global efforts to reduce greenhouse gas emissions.