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Record W4386727124 · doi:10.18280/ijdne.180403

A Comparative Analysis of the Efficiency of Monocrystalline and Polycrystalline Photovoltaic Modules: CTI-80 and YHM-205-27P

2023· article· en· W4386727124 on OpenAlexvenueno aff
Hasan Sh. Majdi, Abdalfadel Younis, Atheer Raheem Abdullah, Monaem Elmnifi, Laith Jaafer Habeeb

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2023
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMonocrystalline siliconMaterials sciencePhotovoltaic systemCrystalliteEngineering physicsEngineeringOptoelectronicsMetallurgyElectrical engineeringSilicon

Abstract

fetched live from OpenAlex

This paper compares the theoretical and actual efficiency of two solar panels, CTI-80 and YHM-205-27P, using Engineering Equation Solver (EES) software.The comparison is based on specific parameters, and the rationale behind their selection is explained.The results show that the CTI-80 panel is more efficient than the YHM-205-27P panel due to the monocrystalline solar cells, which provide more efficiency than polycrystalline solar cells.Although the YHM-205-27P panel has more solar cells, the CTI-80 panel is still more efficient.The temperature affects the theoretical efficiency, as it assumes that all photons have the energy to break the bond in the n-type layer, while in reality, not all photons have sufficient power.Both theoretical and actual efficiencies are lower than the theoretical efficiency due to temperature.The conclusion emphasizes the importance of choosing the right type of solar panel for a particular application based on its efficiency and recommends using monocrystalline solar cells for higher efficiency.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2023
Admission routes1
Has abstractyes

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