Application of passive measures for energy conservation in buildings – a review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".