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Practical Evaluation of Solar Irradiance Effect on PV Performance

2013· article· en· W1604250110 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnergy science and technology · 2013
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPyranometerIrradiancePhotovoltaic systemSolar irradianceEnvironmental scienceSolar micro-inverterRadiationSolar simulatorComputer scienceRelation (database)Remote sensingElectrical engineeringEngineeringOpticsPhysicsMeteorologyMaximum power point trackingVoltageData mining

Abstract

fetched live from OpenAlex

The sun radiation has very important effect on the performance of photovoltaic (PV) solar modules due to its variation from time to time. In this paper, the performance of Solara®-130 PV module will be evaluated practically by using the solar model tester (SMT). The existing SMT can generate variable solar irradiance from 100 w/m2 up to 1050w/m2. The I-V and P-V curves are plotted automatically via software controlling program on the SMT. Different parameters are obtained from the SMT like: VOC, ISC,Vpm, Ipm, Rs, Rsh and fill factor. The results can be used for plotting the relation between the solar irradiances (G) and the corresponding short circuit currents (ISC), then the relation between G-I can be used for measuring the solar irradiance based on the short circuit current in that curve, which means instead of using the PV module for generating electricity, it can be used for measuring the sun radiation instead of using the Pyranometer device.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.285
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