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Photovoltaic Power Generation Model and its Analysis Based on Photovoltaic Power Plant Output Data

2024· article· en· W4406264796 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotovoltaic systemPower (physics)Computer scienceElectricity generationRooftop photovoltaic power stationMaximum power point trackingElectrical engineeringElectronic engineeringEngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

Based on real-time data collected from a specific photovoltaic power plant, mathematical modeling of the electricity output of the photovoltaic power plant is facilitated by combining knowledge of optics and the basic structure of photovoltaic power plants in this paper. The model is composed of two crucial parameters: direct irradiance and environmental enhancement factor, endowing it with not only physical significance but also the capability for numerical estimation and analysis. The constructed photovoltaic power generation model is more in line with actual physical performance, and the comprehensive error between the estimated values and the measured values is only 7%. It can provide a good estimation of radiation and environmental factors.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.261
Teacher spread0.215 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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