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Techno-economic Comparison of Emerging Solar PV modules for Utility Scale PV installation

2021· article· en· W4200358589 on OpenAlexaff
Aruoriwoghene Okere, M. Tariq Iqbal

Bibliographic record

Venue2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · 2021
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPhotovoltaic systemRenewable energySolar energyElectricityPhotovoltaic mounting systemElectricity generationAutomotive engineeringComputer scienceProcess engineeringEnvironmental scienceElectrical engineeringEngineeringReliability engineeringMaximum power point trackingPower (physics)Physics

Abstract

fetched live from OpenAlex

This paper presents a techno-economic analysis of various emerging solar photovoltaic (PV) technologies in modules for possible employment and application to large scale solar energy generation systems. This analysis considered the bifacial technology, half cell technology, and the heterojunction technology. This analysis was based on four important parameters which are generally considered before a PV technology is deployed on a PV farm. These parameters are energy generation, degradation, performance ratio, and installation cost. The simulation and analysis were carried out with the National Renewable Energy Laboratory (NREL) software System Advisor Model (SAM). The results obtained from the simulations showed that for the same environmental conditions and areas with high possibility of light reflection, the bifacial PV technology is a very good candidate. This is due its high efficiency since the bifacial PV module can also convert reflected light on the rear of the solar panel to electricity. However, if the cost of installation is to be considered above every other factor, the heterojunction being the least expensive in the comparison can be employed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.282
Teacher spread0.268 · 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.

Study designOther design
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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venue2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)Same topicPhotovoltaic System Optimization TechniquesFrench-language works237,207