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Energy Yield and Economics of Single-Axis-Tracked Bifacial Photovoltaics with Artificial Ground Reflectors

2023· article· en· W4390188981 on OpenAlexaff
Mandy R. Lewis, Silvana Ovaitt, Byron McDanold, Chris Deline, Karin Hinzer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIrradiancePhotovoltaicsPhotovoltaic systemOpticsRay tracing (physics)Solar irradianceEnvironmental scienceSolar energyCost of electricity by sourceYield (engineering)PhysicsRemote sensingElectrical engineeringEngineeringElectricity generationMeteorologyGeologyPower (physics)

Abstract

fetched live from OpenAlex

Artificial ground reflectors can potentially increase bifacial gain significantly, but their financial viability and ideal configuration are still unclear. We studied the performance of single-axis-tracked bifacial photovoltaic modules through ray-tracing modeling and site field measurements. Reflectors can increase rear irradiance by nearly 140%, front irradiance by 1.1%, and total irradiance by 6.8% over one year. Field measurements demonstrated that reflectors increased daily energy yield up to 6.2%. In both modeling and field tests, the ideal placement of the reflectors was directly under the module due to the significant resulting increase in rear irradiance. Levelized cost of energy calculations demonstrated that reflectors could be financially viable with costs of up to $2-4/m2.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.024
GPT teacher head0.197
Teacher spread0.174 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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