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Record W3209002540 · doi:10.29169/1927-5129.2021.17.08

Study of Wind Speed and Wind Potential at Kagbeni, Thini and Palpa in Nepal

2021· article· en· W3209002540 on OpenAlexvenueno aff
Suresh Prasad Gupta, Binod Adhikar, Dhiraj Lal Yadav

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsWind speedWind powerMeteorologyEnvironmental scienceMathematicsStatisticsGeographyEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.239
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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