Improving Wind Ramp Predictions Using Gabor Filtering And Statistical Scenarios
Bibliographic record
Abstract
The increase of wind penetration into electric power system creates challenges to power grid management due to the variable nature of wind. Unlike conventional power plants, such as thermal, gas or hydro-based plants, wind power generation is not controllable. For example, days of calm weather may suddenly be followed by gusty winds associated with a storm or a front. The current wind power forecasting methodologies, which combine Numerical Weather Prediction (NWP) models and mathematical methods, have been well established during the last decade. However, this forecasting methodology has demonstrated a limited ability to forecast wind ramp events, which are defined as sudden, large changes in wind production. In this study different strategies are developed to improve wind ramp prediction and to provide additional probabilistic information of wind ramp occurrences to end users. First, a methodology of separate wind power predictions based on different weather regimes is presented. Second, an independent wind ramp prediction system is proposed to complement conventional ramp predictions. This system integrates information about the pressure gradient that is extracted by applying Gabor filters to two-dimensional pressure grids. Third, the temporal uncertainty of wind ramp occurrences is addressed using power scenarios generated from quantile forecasts of wind power. The probability of a wind ramp occurrence conditional to the number of scenarios predicting the ramp within certain time intervals is estimated using a logistic regression technique. The proposed strategies were tested on four wind farms located in southern Alberta, Canada, and their performance is discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".