Prediction of ship fuel consumption based on Elastic network regression model

Abstract

Predicting the fuel consumption of ships sailing under different navigation conditions and improving the operation efficiency of shipping industry has become an important topic. There are many characteristic variables affecting ship fuel consumption during navigation, such as trim, draft, wind speed, wind direction and so on. And some variables are highly correlated, which is easy to produce multicollinearity problems. It makes the fuel consumption prediction complex. The study established an Elastic network regression model by combining the least absolute contraction and selection operator (LASSO) and Ridge regression algorithm. The model reduces the complexity and improves the interpretability and accuracy by selecting the characteristic variables affecting ship fuel consumption. The study is verified by the navigation data of a ferry within two months. The results show that compared with long short term memory (LSTM) and back-propagation neural network (BPNN), the Elastic network regression model can not only explain the relationship between fuel consumption and variables, but also predict fuel consumption more accurately and effectively.