Accuracy and applicability of ship's fuel consumption prediction models: A comprehensive comparative analysis

Xi Luo, Ran Yan, Lang Xu and Shuaian Wang

Energy, 2024, vol. 310, issue C

Abstract: The available extensive ship activity data enables employing a complex data-driven resistance-based power model to estimate ship's instantaneous power, and thus the ship's fuel consumption. The Ship Traffic Emissions Assessment Model emerges as a prominent example of such models. However, the performance of the Ship Traffic Emissions Assessment Model in ship's fuel consumption estimation is rarely verified by real fuel consumption data. Hence, this study aims to validate the accuracy of the Ship Traffic Emissions Assessment Model using real fuel consumption data and evaluates its applicability in terms of input features. Situations where the Ship Traffic Emissions Assessment Model shows large errors are identified to analyze its applicability. Furthermore, the Ship Traffic Emissions Assessment Model is compared with other popular fuel consumption prediction models, including the propeller law, the gradient-boosted regression tree model, and two grey box models. A systematical analysis is conducted to evaluate the applicability of various fuel consumption prediction models in different sailing scenarios, providing insights in selecting appropriate models for accurate ship's fuel consumption estimation. The findings contribute to optimizing the ship energy efficiency and facilitating the transition to alternative energy options, ultimately leading to a reduction in greenhouse gas emissions of the maritime industry.

Keywords: Fuel consumption prediction; Ship traffic emissions assessment model (STEAM2); Propeller law; Gradient-boosted regression tree; Serial grey box model; Parallel grey box model (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:310:y:2024:i:c:s0360544224029621

DOI: 10.1016/j.energy.2024.133187

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