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Surface-to-volume ratio: How building geometry impacts solar energy production and heat gain through envelopes

2019· article· en· W2971745848 on OpenAlexaff
Mohamad T. Araji

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSolar gainRenewable energyThermal conductionSolar energyShadingPhotovoltaicsHeat transferBuilding-integrated photovoltaicsGeometryMaterials scienceMechanicsPhysicsOpticsThermodynamicsMathematicsPhotovoltaic systemEngineeringElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract This paper explored the relationship between building geometry and renewable energy production of building-integrated photovoltaics (BIPV). Heat gain was incorporated as a conflicting constraint with respect to energy performance. The building façade was mathematically analyzed by taking into account heat transfer pertaining to site conditions along with different parameters that included shading, orientation, PV tilts (β) and surface-to-volume ratio ( S/V ) as a measure of building compactness. The study involved calculating the impact of each parameter on the convection, conduction and radiation components of the incoming solar energy. S/V was shown to be directly proportional to the amount of solar energy received by the façades and gained by the building in the form of heat. The positive correlation of heat gain with S/V was nearly linear with a slope of around 41.8 kWh/m 2 /m −1 and a mean of approximately 3.4 times more. With the most suitable geometry in terms of net energy gain, S/V of 0.14 m −1 yielded the highest difference between energy production and heat gain. In terms of β, the results demonstrated negative slope of energy production with respect to the tilt at about 2.12 times higher than that displayed by heat gain. Accounting for inter-building effects, a shading reduction equal to d percent can be estimated to an increase of 1.37 d degrees in β at a building consumption of 60 kWh/m 2 .

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.621

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 communication0.0000.001
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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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
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

Citations12
Published2019
Admission routes1
Has abstractyes

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