A fuel consumption prediction model for ships based on historical voyages and meteorological data
Bibliographic record
Abstract
Predicting the fuel consumption of a ship during a voyage is a challenging task, given the internal and external factors that influence it. This challenge has gained crucial importance in light of the regulations imposed by the International Maritime Organization, which aim to reduce greenhouse gas emissions from ships. The objective of this study is to develop a fuel consumption prediction model using data collected from bulk carriers. These predictions will serve as input for a ship routing tool aimed at optimising routes while considering fuel consumption and, consequently, emissions. We propose a data-driven approach to develop a predictive model of fuel consumption for these bulk carriers using a multiple linear regression model considering the propeller rotational speed and with a particular focus on weather factors such as wind, waves and currents, each contributing to the overall speed loss. The results show that the estimated fuel consumption of the studied bulk carriers is strongly affected by the engine setting and the meteorological conditions. The developed model can predict fuel consumption accurately for more than 80% of the voyages of the dataset with a mean absolute error and a root of the mean squared error lower than 0.01 metric ton per nautical mile, and a mean absolute percentage error of less than 15%, making it useful for ship routing purposes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".