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Record W2809614682 · doi:10.1080/17512549.2018.1488617

Application of passive measures for energy conservation in buildings – a review

2018· review· en· W2809614682 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Building Energy Research · 2018
Typereview
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsArchitectural engineeringGlazingBuilding envelopeDaylightingEnergy consumptionPassive coolingPassive solar building designThermal insulationEnergy conservationSolar gainCapital costThermal massEfficient energy useEngineeringEnergy performanceNatural ventilationCivil engineeringMechanical engineeringVentilation (architecture)Solar energyThermal

Abstract

fetched live from OpenAlex

A significant share of the total primary energy belongs to buildings. In many buildings, the energy usage can be significantly reduced by adopting passive strategies. These methods might not need additional capital investment. For instance, an integrated building renovation approach, in which passive methods are implemented, can reduce the energy consumption of building, compensating the additional cost of new technologies. This paper strives to make a technical review of the passive measures in buildings. A categorization of passive energy measures is provided. The review explores several types of insulation materials along with their selection criteria. Application of thermal mass as a redeemable energy technique is also discussed. In addition, performance of different techniques including heating and cooling flow control, optimum place and thickness of insulation, air transport control, water vapour control, natural heating, cooling, and lighting are presented. Advancements in these techniques including the naturally-ventilated envelope, Trombe walls, sunspaces, natural daylighting, sun shading, fenestration, glazing materials and framing, are also discussed. It is concluded that despite their performance in decreasing energy consumption, implementing the most effective combination of these passive technologies, with respect to the characteristics of the buildings, has remained a big challenge for building designers/managers.

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.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.053
GPT teacher head0.390
Teacher spread0.337 · 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