Tidal Current and Level Uncertainty Prediction via Adaptive Linear Programming
Bibliographic record
Abstract
Short-term uncertainty prediction modeling of tidal power generation supports power systems in reserve and regulation markets. In tidal power generation via various tidal energy harvesting technologies, tidal current and level are the most influential factors. This paper addresses a nonparametric prediction interval (NPI)-based uncertainty model thereof. The proposed model adapts a bi-level optimization formulation, based on extreme learning machine (ELM) prediction engine and quantile regression (QR). The quantile probabilities are asymmetrically and adaptively chosen in the upper level optimization to make prediction intervals sharper for a specific reliability level (RL). Besides, the training process of ELM is improved by adaptively selecting ELM's hidden neurons via upper level optimization. The lower level optimization finds ELM's output weighting coefficients through linear programming of QR. The heuristic optimization, consisting of gray wolf optimizer and simplex method, is designed to facilitate the NPI with high exploration and exploitation capabilities in upper level optimization. The performance of the proposed NPI is examined using empirical data recorded in three different sites, located in North America. The results of case studies show that the proposed NPI can provide sharper PIs in comparison to the well-tailored rival models whilst a prespecified RL criterion is met.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".