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Record W2612827029 · doi:10.1109/icit.2017.7913268

Explicit double-exponential modeling methods for photovoltaic cells

2017· article· en· W2612827029 on OpenAlexaff
Ahmad Dehghanzadeh, Gholamreza Farahani, Hani Vahedi, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPhotovoltaic systemDebuggingConfusionComputer scienceProcess (computing)VoltageElectronic engineeringDiodeSelection (genetic algorithm)Exponential functionElectrical engineeringEngineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This paper presents a review on double-diode explicit expressions for current-voltage (I-V) characteristics of photovoltaic (PV) cells. The significance of PV cell modeling is discussed and the shortcomings of the conventional implicit models are examined. The explicit models are favorable for real time operation during designing and debugging process for efficiency enhancement of PV systems. This paper supports or contradicts some reported methods in the literature for their capability to recreate I-V behavior of common industrial PV cells. Moreover, it would comprehensively address confusion among students and PV engineers with regard to the selection of appropriate model for repeated simulations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.081
GPT teacher head0.392
Teacher spread0.311 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations2
Published2017
Admission routes1
Has abstractyes

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