Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE
Bibliographic record
Abstract
The present paper bridges mathematical modeling and wind resource assessment (WRA). Sensitivity analysis (SA) links portions of output variance to the variance in each input variable. Global SA (GSA) explores inputs globally. One-at-a-time SA is dominating in WRA, while GSA is often overlooked. Compared to traditional methods, GSA offers potential improvement by the means of the quasi-Monte Carlo (QMC) technique with its elaborate sampling designs enabling faster convergence. The main novelty of this work is twofold: the use of QMC in WRA and Sobol method as a variance-based GSA method in WRA. This paper is among a few using GSA in WRA. Two case studies were conducted. One shows that QMC with sampling based on Sobol sampling outperforms Latin hypercube sampling and pseudorandom sampling. It also shows that in terms of accuracy of results, the brute-force calculation of Sobol sensitivity indices (SI) should be used whenever the model allows it; otherwise, SI can be estimated. Another case study demonstrates a valid GSA study for WRA at Masdar City, United Arab Emirates. The results suggest that the influence of the variance in Weibull parameters on annual energy production (AEP) might be overestimated, while found to be responsible for 2% of AEP uncertainty, and the influence of the variance in air density might be overlooked, while found to account for 94%. WRA studies would benefit greatly from using the QMC and Sobol method. The Sobol method is a universal GSA method, providing valid results for nonlinear models typical for WRA, and QMC provides global scalability, model independence, and flexibility in uncertainty quantification.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| 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".