Predicting and Optimising Ship Fuel Consumption Using Data-Driven Models and a Proposed IGWO Algorithm for Speed Adjustment
Bibliographic record
Abstract
Abstract As international climate policies become more stringent, accurate prediction and optimisation of fuel oil consumption (FOC) are now crucial for analysis of a ship’s navigation status, energy conservation, and reductions in greenhouse gas emissions. This study presents two approaches to FOC prediction (using real-time and time-series methods) and a framework for FOC optimisation through analysis of operational data and sailing speed adjustments for a container ship. XGBoost, an ensemble learning model, and Meta-BiLSTM, a deep learning model based on stacking theory, perform exceptionally well in FOC prediction, achieving mean squared errors of 0.04% and 0.07%, respectively. The ship’s route is optimally clustered based on meteorological data, ensuring continuity of the route within each cluster. An FOC prediction model is integrated with the proposed improved grey wolf optimiser (IGWO) algorithm to reduce FOC by adjusting the optimal sailing speed for each cluster along the route. For the ship studied here, an FOC reduction of 4.54% is achieved, equivalent to 33.14 tons. The speed optimisation method employed in this research appears to be more practical under operational conditions than alternative methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".