Machine learning models for predicting ship main engine Fuel Oil Consumption : a comparative study

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2019) Machine learning models for predicting ship main engine Fuel Oil Consumption : a comparative study. Ocean Engineering, 188. 106282. ISSN 0029-8018 (https://doi.org/10.1016/j.oceaneng.2019.106282)

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Abstract

As Fuel Oil Consumption (FOC) constitutes over 25% of a vessel’s overall operating cost, its accurate forecasting, and the reliable prediction of the relevant ship operating expenditures can majorly impact the ship operation sustainability and profitability. This study presents a comparison of data-driven, multiple regression algorithms for predicting ship main engine FOC considering two different shipboard data acquisition strategies, noon-reports and Automated Data Logging & Monitoring (ADLM) systems. For this, various multiple regression algorithms including Support Vector Machines (SVMs), Random Forest Regressors (RFRs), Extra Trees Regressors (ETRs), Artificial Neural Networks (ANNs), and ensemble methods are employed. The effectiveness of the tested algorithms is investigated based on a number of key performance indicators, such as the mean and median average error and the coefficient of determination (R2). ETR and RFR models were found to perform best in both cases, whilst the existence of an ADLM system increased accuracy by 7% and reduced the required period for data collection by up to 90%. The derived models can accurately predict the FOC of vessels sailing under different load conditions, weather conditions, speed, sailing distance, and drafts.

ORCID iDs

Gkerekos, Christos ORCID logoORCID: https://orcid.org/0000-0002-3278-9806, Lazakis, Iraklis ORCID logoORCID: https://orcid.org/0000-0002-6130-9410 and Theotokatos, Gerasimos ORCID logoORCID: https://orcid.org/0000-0003-3547-8867;
  • Item type:Article
    ID code:70040
    Dates:
    Date
    Event
    15 September 2019
    Published
    26 August 2019
    Published Online
    5 August 2019
    Accepted
    2019
    Submitted
    Keywords:FOC prediction, ship energy effciency, multiple regression, support vector machines, neural networks, ensemble methods, machine learning, Naval architecture. Shipbuilding. Marine engineering, Ocean Engineering, Environmental Engineering
    Subjects:Naval Science > Naval architecture. Shipbuilding. Marine engineering
    Department:Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering
    Depositing user: Pure Administrator
    Date deposited:07 Oct 2019 13:38
    Last modified:30 Nov 2023 14:30
    URI:https://strathprints.strath.ac.uk/id/eprint/70040
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