A Novel Study for Machine-Learning-Based Ship Energy Demand Forecasting in Container Port
Alper Seyhan
Abstract
Maritime transportation is crucial for global trade, yet it is a significant source of emissions. This study aims to enhance the operational efficiency and sustainability of container ports by accurately estimating energy needs. Analyzing data from 440 ships visiting a container port within a year, including parameters such as main engine (ME) power, auxiliary engine (AE) power, gross registered tonnage (GRT), twenty-foot equivalent unit (TEU), and hoteling time, regression analysis techniques were employed within MATLAB’s Regression Learner App. The model predicted future energy demands with an accuracy of 82%, providing a robust framework for energy management and infrastructure investment. The strategic planning based on these predictions supports sustainability goals and enhances energy supply reliability. The study highlights the dual benefit for port and ship owners in precise energy need assessments, enabling cost-effective energy management. This research offers valuable insights for stakeholders, paving the way for greener and more efficient port operations.