Study of Wind Speed and Wind Potential at Kagbeni, Thini and Palpa in Nepal
Bibliographic record
Abstract
The primary objective of this paper is to study and analyze the wind characteristics and power potential at the three different places in Nepal. One year of wind speed data measured at 10 m and 20m height above ground level, provided by the Department of Alternative Energy Promotion Center, have been analyzed in this study. Direct use of data including the mathematics of probability and statistics has been applied to compute the wind power potential of the proposed sites with the occurrence of effective wind speed between cut-in and cut-out speed. The diurnal wind speed variation analysis of the three different sites showed that higher wind speed occurred during the daytime and reached a maximum at 3 PM whereas the lowest wind speed occurred after midnight and achieved a minimum at 7 AM to 8 AM. On basis of wind energy potential, Kagbeni has an annual potential energy of 3.98MWhr/m2 at 10m height and 4.82MWhr/m2 at 20m height while Palpa has the potential of 0.27MWhr/m2 and 0.36MWhr/m2 at the two heights with wind speed more or equal to 3m/s. Similarly, Thini has potential of 2.4MWhr/m2 and 2.9MWhr/m2 at 10m and 20m height on the limit of above wind speed. On the monthly basis, Kagbeni and Thini have the highest average wind speed in June whereas Palpa has in March and April. Likewise the highest value of wind speed at Kagbeni, Palpa and Thani are found as 22.53m/s and 21.75m/s; 17.66m/s and 17.11m/s, and 17.9m/s and 7.3m/s in April and March at heights of 10m and 20m respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".