A Deterministic Bases Piecewise Wind Power Forecasting Models
Bibliographic record
Abstract
Continue emphasis in mitigating the environmental impacts of fossil generated electrical energy has fuelled interest in sustainable and renewable energy; as a result of this interest, renewable energy penetration into power utilities energy mix has increased significantly. Two major issues delaying further increase of renewable energy are supply intermittency and availability. Prediction of renewable energy availability can never be over emphasized. In this paper we propose a simple nonlinear least square piecewise model to predict output power of a small Canadian wind farm. The proposed model decomposes the wind speed sweeping the wind turbine into three major speed groups, slow, moderate and fast speed. The dynamics of the wind speed in each group defines the model and the prediction error performance. We showed that the piecewise model outperformed the manufacturer’s power curve that is traditionally uses by wind farms. We present typical predictions for Fall, Winter, Spring and Summer and compared results from our proposed model to the manufacturer’s power curve. The piecewise model as well as the manufacturer’s power curve performances are both related to the skill of the wind speed estimator, accurate wind speed estimates will result to excellent forecast for both models.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".