MétaCan
Menu
Back to cohort
Record W2461149026 · doi:10.6000/1927-5129.2016.12.39

Feasibility Study for the Installation of Wind Energy Conversion Systems in Pakistan

2016· article· en· W2461149026 on OpenAlexvenueno aff
Saif Ur Rehman, Muhammad Shoaib, Shamim Khan, Muhammad Jahangir

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerNet present valueElectricity generationElectricityEnvironmental scienceEconomic evaluationEconomic feasibilityPayback periodEconomic analysisNameplate capacityInternal rate of returnProduction (economics)Agricultural economicsEngineeringPower (physics)EconomicsElectrical engineering

Abstract

fetched live from OpenAlex

Wind generated electricity is one of the most attractive methods of electric power generation in a third world country like Pakistan. This paper presents the economic and technical viability of wind energy generation in remote and rural areas of Sindh (Pakistan). In light of this, Three wind turbines with power ratings of 660 kW, 1,000 kW and 18,00 kW are chosen. The economic analysis has been carried out by four economic indexes that are: cost benefit analysis (CBI), Net Present Value (NPV), Pay Back Period (PBP) and the Costof Energy (COE $/kWh). The wind speed data for this study was obtained from the Pakistan meteorological | department, measured at heights of 10m and 30 m, that span from 2002 to 2005.The outcome of the study showed that, the highest annual average energy of 5396 MWh/yr could be generated by Vestas V80 (with power capacity of 1.8 MW). Furthermore, the baseline economic evaluation of all the selected turbines, indicted that V80-1.8 MW gave the least cost (0.043 $/KWh) of electricity production at 80m hub height while the Sensitivity of the selected parameters showed that NPV is more sensitive to retail price of local electricity cost.

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)0.002
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 score0.596
Threshold uncertainty score0.107

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.031
GPT teacher head0.286
Teacher spread0.255 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Basic & Applied SciencesSame topicWind Energy Research and DevelopmentFrench-language works237,207