Assessment of Machine Learning Procedures for Forecasting Ship Fuel Ingestion

Abstract

The maritime industry is facing increasing pressure to enhance operational efficiency and reduce environmental impact, particularly in the context of fuel consumption and emissions. This study investigates the applicability and effectiveness of machine learning (ML) procedures for forecasting ship fuel ingestion, a critical parameter in assessing vessel performance. A comprehensive dataset comprising historical fuel consumption records, meteorological data, and operational parameters from diverse maritime settings is utilized. Various ML algorithms, including regression models, support vector machines, and neural networks, are implemented and evaluated for their accuracy and reliability in predicting ship fuel ingestion. The assessment considers different operational conditions, vessel types, and geographical regions to capture the complexities of maritime operations. Feature importance analysis is conducted to identify key variables influencing fuel consumption, providing insights into the underlying factors affecting maritime energy efficiency. The results indicate that certain ML approaches demonstrate superior forecasting capabilities compared to traditional methods. Furthermore, the study explores the challenges and limitations associated with applying ML to maritime fuel consumption forecasting, including data quality issues, model interpretability, and scalability. Recommendations for addressing these challenges and improving the overall performance of ML-based forecasting models in the maritime domain are provided. This research contributes to the growing body of literature on the integration of machine learning techniques in maritime operations, offering valuable insights for ship operators, policymakers, and researchers seeking to optimize fuel consumption and reduce the environmental impact of maritime activities.