Machine Learning‐Assisted Renewable Energy Uncertainty Compensation With Demand Response: An Analysis of Ship Energy Systems
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
The integration of solar power systems into cruising ships is gaining popularity in the marine sector due to the restrictions imposed by the Marine Pollution Protocol and the rapid growth of photovoltaic (PV) technology. However, this integration brings various challenges in the operation of the ship energy system, including resource uncertainty, power imbalance, and reduced service reliability. Therefore, this study proposes a novel three‐stage operation strategy for ship multienergy systems to compensate for the uncertainty of PV generation. In Stage 1, a day‐ahead scheduling process is performed to determine the setpoints of major system components. The goal is to minimize operating costs while meeting electrical, heating, and cooling demands. In Stage 2, a deep neural network‐based PV prediction model is developed. Particle swarm optimization is used to achieve fast convergence and high accuracy. A detailed statistical analysis is then applied for early detection of data drift, which may cause a significant drop in prediction accuracy. The uncertainty of PV output is then estimated based on the new trends observed in the incoming dataset. In Stage 3, a demand response (DR)‐based scheme is introduced to compensate for the uncertainty of PV power, identified in Stage 2. The DR programs allow sharing the load demand among different intervals by adjusting controllable loads. As a result, the amount of power mismatches caused by the uncertainty factor has decreased. Finally, simulation results also demonstrate that the amount of load shedding requirement in the ship energy system is significantly reduced using the proposed method.
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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 | 0.001 | 0.003 |
| 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 it