A Novel Study for Machine-Learning-Based Ship Energy Demand Forecasting in Container Port

Alper Seyhan ()
Additional contact information
Alper Seyhan: Department of Maritime Transportation and Management Engineering, Zonguldak Bülent Ecevit University, Zonguldak 67300, Turkey

Sustainability, 2025, vol. 17, issue 12, 1-14

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.

Keywords: container ports; energy estimation; regression analysis; MATLAB; energy management; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/12/5612/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/12/5612/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:12:p:5612-:d:1681915

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().