A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications
Bibliographic record
Abstract
One of the green, clean, and environment-friendly sources of energy is wind energy. For the assessment of wind energy potential, the parameters of the probability distribution function (PDF), i.e., Weibull distribution (WD), that fits well with the wind speed data must be known. In this research, we proposed a novel optimized energy pattern factor method (NOEPFM) based on the trust-region-dogleg algorithm and applied it to wind speed data of four cities of the Southern region of Punjab, Pakistan, to determine WD parameters, i.e., shape k and scale c parameters. In order to authenticate the practicability of the proposed NOEPFM, it is compared with the other existing energy pattern factor (EPF)-based methods such as the energy pattern factor method (EPFM), Sathyajith's EPFM (EPFMS), and novel EPFM (NEPFM). The performance of NOEPFM is measured in terms of five goodness-of-fit indices, namely root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), coefficient of efficiency (CoE), and maximum absolute error (MaxAE). Numerical results reveal that the NOEPFM method was the best fit compared to the other EPFMs for all the considered wind speed datasets. This justifies the workability of the proposed NOEPFM and can serve as an enhanced approach for calculating wind power potential.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".