Development of a Wind to Power Model for Wind Farm Power Production Forecasting
Bibliographic record
Abstract
The objective of this study is to develop a Wind to Power forecasting methodology where wind farm power curves are used to convert the predicted wind speeds to predicted power productions of wind farms. A methodology is proposed to develop a wind farm power curve by performing several experiments using historical wind farm power productions and wind measurements. A simple method, the Bins Method, is compared to an advanced method based on Artificial Neural Networks (ANN). It is shown that the advanced method does not have significant advantages in terms of accuracy. However, due to its flexibility on the choice of input parameters, the ANN method is best suited for studies such as the optimal selection of input parameters. For this reason, these two methods are used alternatively in this study according to the applications. The influence of different meteorological parameters on the wind farm power curve has also been investigated with the ANN method. The wind farm power curves developed in this work have been tested for the prediction of the wind power productions using forecasted wind speed data obtained from a Numerical Weather Prediction model (GEM-LAM) as input variables to the Wind to Power model. The predicted power productions compare well with those recorded at the wind farm. The proposed method is applied to a second operating wind farm to demonstrate its validity.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".