Comparison of methods for building a capacity model in generation capacity adequacy studies
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Bibliographic record
Abstract
The authors provide a comparison of different methods of preparing a capacity outage probability distribution in generating capacity adequacy studies. The recursive method, also referred to as the unit addition algorithm, with different rounding steps and rounding methods is analyzed. Capacity rounding techniques are typically utilized when dealing with units of noninteger capacities to reduce execution time. A new capacity rounding technique is proposed, in which the outage capacities of a unit are rounded to the nearest capacity steps before the unit is added to the system. It is found that this method yields very good results while requiring less computing time. The cumulant method based on the well-known Gram-Charlier or Edgeworth expansion is studied and compared with the recursive method. The convergence behavior of the cumulant method as a function of unit forced outage rate (FOR) values is examined. The accuracy and computing requirements of each method are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 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) | 0.000 | 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