Study on Maximum Power Limitation of Gillnet Fishing Vessels Based on EEXI

Abstract

To address issues such as insufficient fishing vessel data records, low energy efficiency, and high emissions, this study constructs and validates a predictive model for the maximum power limitation of fishing vessels. Using gillnet fishing vessels as a case study, the Energy Efficiency Existing Ship Index (EEXI) reference line formula for gillnet fishing vessels is fitted using the nonlinear least squares method. Sensitivity analysis reveals power’s impact on fishing vessels’ energy efficiency. The maximum power calculation method for fishing vessels is derived by combining the EEXI calculation formula, the reference line formula, and the power-speed relationship. Three regression prediction models—Decision Tree, Random Forest, and Gradient Boosting—are used to construct prediction models with gross tonnage, length between perpendiculars, and gross tonnage and length between perpendiculars as inputs, respectively. Results show that power significantly impacts the energy efficiency of fishing vessels. The EEXI reference line formula for gillnet fishing vessels has MAE, MSE, MAPE, RMSE, and R^2 values of 13.3518, 369.5200, 18%, 19.2229, and 0.6366, respectively. The Random Forest regression model with gross tonnage as input performs best in predicting the maximum power limitation of gillnet fishing vessels, with MAE, MSE, MAPE, RMSE, and R^2 values of 5.61423, 6152.40982, 1.90888%, 78.43730, and 0.633062393, respectively. This paper provides a reliable calculation method and prediction model for the maximum power limitation of fishing vessels, offering systematic technical support and decision-making references for limiting the maximum power of fishing vessels.