Evaluation of wind power commitment risk in system operation
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 environmental concerns associated with electricity generation and the increased public awareness of renewable energy resources have resulted in world wide and rapid growth of wind power installations. Wind power generation is uncertain, fluctuating and intermittent. It is a major challenge to maintain reliability while operating a power system with significant wind power penetration. The system operator is required to commit an appropriate amount of wind power in combination with other generating units to satisfy the forecast load with acceptable reliability in the lead time considered. Accurate wind power forecasting plays a vital role in estimating the wind power contribution in the specified lead time. The wind power generation in the next hour or next few hours depends upon the initial wind power at the wind site. There is a probability that the actual wind power will be less than the predicted value. This probability can be designated as wind power commitment risk. This paper presents a conditional probability approach to quantify the short term wind power commitment risk using a statistical time series model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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