An Operational Application of NWP Models in a Wind Power Forecasting Demonstration Experiment
Why this work is in the frame
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Bibliographic record
Abstract
Environment Canada (EC) and Hydro-Québec (HQ) have been collaborating in a Research & Development and Demonstration project on a high resolution wind energy dedicated forecasting system (SPÉO: Système de Prévision ÉOlien under its French acronym). This project emphasizes the operational tests and the forecast of high impact events, e.g. wind ramps. It was found that SPÉO improves the Canadian Regional Deterministic Prediction System (RDPS), by about 18% in terms of the RMSE (Root Mean Square Error) of the predicted wind speed when compared with mast observations from three wind power plants. The improvement is most significant in the cold season. When the average wind speed measured at all wind turbines (nacelle anemometer) is used as a reference, SPÉO improves the RMSE of the average wind speed at a wind power plant in complex terrain (24%) compared with that of RDPS. However, there is almost no improvement for two other wind power plants located in less complex terrain. The average wind speed is corrected with the average wind speed measured at all turbines, and is then fed into a wind-to-power conversion module for power production forecasts. The power production forecast is improved by 6% on average in complex terrain when SPÉO winds are used as input compared to the RDPS. The most important finding of this project is SPÉO's ability to predict ramps due to mountain waves/downslope winds. The proposed forecast index for ramps based on the Froude number is useful for predicting the onset of this kind of ramp when a high resolution NWP model is unavailable.
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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 | 0.000 | 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