An Operational Application of NWP Models in a Wind Power Forecasting Demonstration Experiment
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.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".