Prediction of Daily Fuel Consumption of Ship Based on LASSO

Currently published or used fuel consumption prediction algorithms have drawbacks, such as low interpretability and poor generalization ability for the ship fuel consumption data set, which features high dimensionality and sparsity. A prediction algorithm based on least absolute shrinkage and selection operator (LASSO) is introduced to improve them. The application of the method is demonstrated with a set of fuel consumption data from a cargo ship's noon reports. The result is compared to those with Ridge regression, ordinary least squares method and artificial network, which shows that the average MSE with LASSO is 0.07, 0.08, 3.77 lower, respectively.