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Record W3038692405 · doi:10.22059/ees.2020.43228

Net zero energy buildings in semi-arid climates: An analysis on 3 case studies in Tehran, Iran

2020· article· en· W3038692405 on OpenAlexaff
Delara Karimi, Babak Ghorbani, Mahyar Momen

Bibliographic record

VenueEnvironmental Engineering Science · 2020
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsZero-energy buildingRenewable energyAridEnvironmental scienceEnvironmental engineeringCivil engineeringDesert climateEngineeringGeology

Abstract

fetched live from OpenAlex

This paper analyzes utilization of renewable energy systems and efficient building envelopes in the semi-arid climate. The proposed model evaluates renewable energy systems solutions as well as economic- and energy-efficient construction materials for the net zero-energy buildings (NZEB) in semi-arid climates. The objective of this paper is to optimize total energy cost and environmental impacts in NZEB. Three real case studies in Tehran, Iran, are used for this analysis. Different potential renewable energy systems, including PV panels, solar thermosiphon systems, geothermal heat pumps, and their combinations, are investigated in two residential buildings and a commercial building. Moreover, analyzing building envelopes using thermodynamic characteristics of building surfaces is done. The results show that the implementation of the proposed model in the buildings in semi-arid climates significantly reduces the negative environmental impacts for both residential and commercial buildings, and also increasing their energy efficiency up to 63% and 38%, respectively.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

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