Machine learning-based power prediction for the icebreaker Henry Larsen using vessel motions and environmental data in open water conditions
Bibliographic record
Abstract
Accurate power prediction for a ship vessels is critical for improving operational efficiency and reducing fuel consumption, hence, shipborne emissions. This paper presents a machine learning approach for predicting the required power for ship at a given operational / environmental conditions using time series data that combines vessel telemetry, including speed and motions, with environmental data from ECMWF (European Centre for Medium-Range Weather Forecasts). Focusing on open water conditions to avoid ice interference, we apply ensemble methods, specifically Random Forest and XGBoost (Extreme Gradient Boost), and evaluate their performance using Time Series Split and Block Time Series Split techniques to handle temporal dependencies. Our models are assessed using Root Mean Squared Error, Mean Absolute Error, and R-squared. In this paper, the Canadian Coast Guard Vessel is used as a case study. The results demonstrate the effectiveness of machine learning in predicting vessel power and highlight the importance of selecting appropriate data splitting strategies to prevent data leakage. Index Terms—Machine Learning, Power Prediction, Time series split, Ice breaker vessel motion, Data leakage.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".