Predicting and Optimising Ship Fuel Consumption Using Data-Driven Models and a Proposed IGWO Algorithm for Speed Adjustment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it