Fuel Consumption Prediction and Optimization Model for Pure Car/Truck Transport Ships

Abstract

Predicting and optimizing ship fuel use is a crucial technology for lowering greenhouse gas emissions. Unfortunately, existing research is rarely capable of developing fuel consumption forecasts and optimization models for a particular transport system. This study develops a fuel consumption prediction model based on machine learning and a fuel consumption optimization model based on particle swarm optimization for ships. We studied nearly ten years of big data from a large Korean pure car and truck shipping company (PCTC), which contained 16,189 observations from 2012 to 2021. Results indicate that the XGBoost deep learning model outperforms conventional prediction models at the stage of fuel consumption prediction, with an R2 of 0.97. Furthermore, in the fuel consumption optimization stage, the particle swarm optimization method can effectively reduce fuel consumption. This study helps PCTC companies control shipping costs and save energy. Insights for shipping businesses to meet environmental demands are provided as well.

Keywords

• fuel consumption prediction
• fuel consumption optimization
• machine learning
• maritime
• XGBoost
• particle swarm optimization