Assessment of Wind Speed and Power Density Using Weibull and Rayleigh Distributions at Turbat, Balochistan, Pakistan
Bibliographic record
Abstract
The meager economic situation, indefinite energy crisis, industrial modernization, and hazardous contamination impulse the empiricist to transcend the attention on renewable energy resources in Pakistan. One of the most rapidly growing renewable energy sources is wind energy. The main goal of this research work is to examine the wind characteristics and wind potential at the site of Turbat, Balochistan, Pakistan. For this purpose, the measured hourly time series data was collected from the Pakistan Meteorological Department (CPDC, Karachi) for 21 months (Jan 2012 – Sep 2013). After evaluating the monthly average wind speed (> 4 m/s), the average value of most probable wind speed (3.83 m/s), the average value of wind speed carrying maximum energy (7.732 m/s), and the standard deviation of the data (1.699 – 3.306), the results are used to statistically evaluate the data by Weibull and Rayleigh distributions for the selected site. The monthly average value of wind power and energy densities of the selected site is 140.145 W/m2 and 101.775 kWh/m2, respectively. A comparison was made between the mean power potential of the site and the power potential assessed using the Weibull and Rayleigh distributions. It was revealed that the Weibull distribution depicted the data more accurately. This statement is further enriched by the assessment of the performance of both distributions with the RMSE, χ2, and R2 tests
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".