Machine learning approach to ship fuel consumption: A case of container vessel. (July 2020) - Uyanık, Tayfun; Karatuğ, Çağlar; Arslanoğlu, Yasin
Highlights: Correlation analysis is performed between input variables and fuel consumption. Various machine learning methods are applied to fuel oil consumption of vessel. Algorithm hyperparameters tuned for better prediction. Comparison between estimation results taken from each methods are made. Various error metrics is used for the evaluation of the estimation models. Abstract: An improvement of the marine vessel's fuel consumption will provide efficiency and profitability in ship management since fuel cost is one of the biggest operating cost. However, estimation of the fuel consumption of marine vessels is a difficult issue, because the fuel consumption rate of the vessel is directly dependent on multiple external factors such as the condition of the main engine, cargo weight, ship draft, sea condition, weather condition, etc. Nowadays, statistical models have been established based on actual ship data, and the fuel consumption of the vessel has been tried to be estimated as best as possible. In this study, various prediction models such as Multiple Linear Regression, Ridge and LASSO Regression, Support Vector Regression, Tree-Based Algorithms, Boosting Algorithms have been established for a container ship. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, coefficient of determination are employed in order to evaluate the correctness of estimation models and correlation analysis between variables is accomplished. Parameters such as main engine rpm, main engine cylinder values, scavenge air, shaft indicators are found highly correlated with fuel consumption. Under the influence of various external factors on fuel consumption, the nearest estimation of the actual fuel consumption data is made by multiple linear regression and ridge regression with 0.0001 root mean square error, 0.002 mean absolute error and %99.9 coefficient of determination score.