Greenhouse Gas Emissions Reduction of a Hybrid-Powered Ferry using Deep Reinforcement Learning for Power Load Distribution
Bibliographic record
Abstract
This article explores the use of the twin delayed deep deterministic policy gradient (TD3), a deep reinforcement learning algorithm, to reduce the cumulative greenhouse gas (GHG) emissions from the sailing trips of a hybrid-powered roll-on roll-of liquefied natural gas ferry. The objective of the algorithm is to optimally control the power load distribution between the ferry’s engines and battery to achieve a reduction in GHG emissions. Results from this study show that the TD3 agent achieved an average reduction in cumulative GHG emissions by 5% against actual operations for the sailing trips that were analyzed. The performance of the TD3 agent was compared to a rule-based energy management strategy (EMS) in which the ferry’s battery was used to operate the ferry completely at low load operations and provide surplus power when the power demand was greater than the engine rating. The rule-based EMS failed to achieve GHG emissions reductions against actual operations thereby indicating the efficacy of the TD3 agent in achieving emissions reductions.
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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) | 0.000 | 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) | 0.001 | 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".