Minimum Open Data Subset for Wind Power Prediction
Bibliographic record
Abstract
Abstract. Accurate wind power prediction is required for grid integration of renewables, minimizing curtailment of renewable energy, and performing resource assessments. Prior research has explored the use of numerical weather prediction, reanalysis datasets, and observational data in power prediction and resource assessment applications. Observational data is spatially limited and often proprietary. Reanalysis datasets are available globally, but have a large spatial resolution and therefore do not capture the effects of complex geography well. Numerical weather prediction simulations allow for high spatial resolution flow models, but require significant processing resources and computational time. This work combines historical wind power production data, observational data, MERRA-2 reanalysis, and WRF model data at three wind farms in Ontario, Canada to determine the optimal data source, combination of data sources, and variables for prediction of wind power using a random forests model. Results show that a model combining select data from all three data sources, including a combination of wind speed, time, and other weather variables, improves predictive performance by up to 57 % over the benchmark power curve model. Analysis of feature importance shows that aggregating wind speed allows the model to make better use of additional weather features. The minimum subset of input data for the best performing model, which achieves a mean absolute error (MAE) of 0.071 across all sites, consists of averaged wind speed, temperature, wind direction, pressure, air density, and time variables (hour, day and month).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".