aceAn integrated method of advanced optimisation and adaptive ensemble learning for ship fuel consumption prediction
• Proposes an adaptive cluster-based multi-ensemble (ACME) model for robust ship fuel consumption prediction. • Develops a SHAP-weighted multi-model feature selection (SWFS) algorithm for dimensionality reduction. • Introduces a hierarchical adaptive parameter space exploration (HAPSE) method for efficient tuning. • Establishes a dual-layer SHAP framework for global and local model interpretability. • Integrates multi-source data fusion to enhance prediction accuracy and operational relevance. Accurate predictio…
The integrated ACME framework increased ship fuel consumption prediction accuracy and generalisation, outperforming mainstream models and providing data-driven tools for energy efficiency management and decarbonisation in maritime transport.
Evidence
- Peer-reviewedTransportation Research Part C: Emerging Technologies2026-04-02
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Truvace Impact Record TRV-2026-0150, v1: “An integrated method of advanced optimisation and adaptive ensemble learning for ship fuel consumption prediction.” Truvace, 2026-07-13. /record/TRV-2026-0150 (accessed at citation time). sha256 279d5660d3accca4…
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