Bibliographic record
Abstract
This paper presents new approaches for short-term wind power forecasts developed by the authors. Day-ahead and hours-ahead wind power forecasts were derived from the wind speed forecast data generated by High Resolution Deterministic Prediction System (HRDPS) Model at the Environment and Climate Change Canada (ECCC). Following a statistical analysis to verify the accuracy of the ECCC wind speed forecasts themselves, a power curve transfer model was developed to offer day-ahead wind power forecasts by converting the ECCC wind speed forecasts to wind power forecasts. An hours-ahead wind speed forecasting method was developed using a fusion approach to predict wind power for look-ahead times ranging from 30 minutes to six and a half hours with 5-min time steps to meet forecast delivery requirements of utilities and system operators. Using operational data over several years from six wind farms in different locations in Canada, the forecasting methodologies were validated for their good performance on the basis of statistical metrics and error distribution analyses.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".