EconPapers    
Economics at your fingertips  
 

Multi-dimensional performance verification of ship fuel consumption prediction model under dynamic operating conditions

Ailong Fan, Yifu Wang, Zhihui Hu, Liu Yang, Xuelong Fan and Zhiyong Yang

Energy, 2025, vol. 332, issue C

Abstract: Given stringent decarbonization targets in the global shipping industry, validating the accuracy and applicability of fuel-consumption prediction models using actual navigation data is critical for informed energy optimization, cost control, and emission management. To address shortcomings of existing grey-box models in cross-condition verification and adaptability assessment, this study develops a systematic, multi-dimensional testing framework. The framework is for a classified weighted fusion strategy-based grey-box model (CWFM), encompassing both single and representative combined operating conditions. Model performance is comprehensively evaluated using root mean square error (RMSE), coefficient of determination (R2), coefficient of variation (CV), and generalization error (GE). A case study demonstrates that, under combined operating conditions, CWFM reduces average RMSE by 0.28–0.35 kg/h versus the optimal sub-model, improves R2 by 5.5 %–7.5 %, narrows confidence-interval widths by 43.6 %, maintains CV below 0.4 %, and validates the dynamic weight allocation strategy's effectiveness in enhancing prediction performance. Furthermore, cross-operating conditions GE is 83.1 % lower than that of traditional mechanistic models; the RMSE ratios under combined operating conditions (0.31–0.40) outperforming comparison models. The developed framework ensures that fuel consumption prediction models adequately meet the actual operational requirements of ships, providing for achieving the goals of green shipping.

Keywords: Ship fuel consumption prediction; Grey-box model; Performance verification; Dynamic condition; Multi-dimensional indicators (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225027628
Full text for ScienceDirect subscribers only

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:eee:energy:v:332:y:2025:i:c:s0360544225027628

DOI: 10.1016/j.energy.2025.137120

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-07-15
Handle: RePEc:eee:energy:v:332:y:2025:i:c:s0360544225027628