Intelligent estimation of the wake losses in wind farms : Artificial neural network estimation of the power of a wind farm considering the effect of wake losses
Bibliographic record
Abstract
The transition from non-renewable to renewable energy production requires a detailed optimization and quantification of the generated power. The loss of power due to wake effect is a common problem for wind farms. The wake effect is the reduction of velocity and increase of turbulence in the wind flow downstream from a wind turbine. The wake effect is a complex multivariable phenomenon and its understanding iscapital forappropriate estimations of the power of a wind field and its turbines.This thesis builds an artificial neural network based on machine learning to model the performance of a single wind farm owned by WEICAN (Canada) taking into account the wake losses. Four different models have been considered. The first is not accounting for the wake losses; the second considers only the wake of the closest turbines; the third takes into account the wake in all the turbines; and the fourth provides all the data to the program in order to see what it can doon its own. The performance is evaluated using the mean absolute error, the root mean squared error and the normalized root mean square error.The best results areobtained using the third model, hence showing that the wake loss is significant and must be considered in the model. It is proved that with the appropriate input variables, an artificial neural network can predict the power of a wind farm accounting for the wake losses. The best performance of the artificial neural network is obtained for wind speeds up to 14 m/s.
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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) | 0.001 | 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 | 0.000 |
| 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 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".