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Record W4210579153 · doi:10.1002/9781119736592.ch1

Introduction

2022· other· en· W4210579153 on OpenAlexaff
Davood Naghaviha, Hassan Nikkhajoei, Houshang Karimi

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPhotovoltaic systemScale (ratio)Solar energyPower stationGrid-connected photovoltaic power systemTransmission (telecommunications)Solar powerEnvironmental sciencePower (physics)Electrical engineeringEngineeringPhysicsMaximum power point tracking

Abstract

fetched live from OpenAlex

The source of solar energy is the sun. Solar energy can be classified as heat which is generated by electromagnetic waves, and light which is produced by photons. Solar energy is used for two groups of applications: non-power plant and power plant. The photovoltaic (PV) power plants can be categorized into four groups based on their output power: small-scale, medium-size, large-scale, and very large-scale PV power plant. The large-scale PV plants are known as solar farms and the very large-scale PV plants are commonly known as solar parks. In addition to a distribution substation, the large-scale and very-large-scale PV plants usually have one or more transmission or sub-transmission substations. This chapter discusses some of the advantages and disadvantages of the grid-connected PV plants. It also presents an overview of the key concepts discussed in this book.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.721
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.7220.001

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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