Optimum envelope design toward zero energy buildings in Iran
Bibliographic record
Abstract
Buildings, commercial and residential combined, account for around 40% of total energy consumption in Iran. Energy consumption in buildings is predicted to increase in the the coming decades decades and immediate actions are required to meet the needs of future generations. Zero energy building (ZEB) is an important concept that can help nations to have a sustainable future. An important step for building a ZEB is to minimize the energy lost from the building, and the envelope of buildings plays a vital role in achieving minimal energy losses. In this paper, a life cycle cost (LCC) analysis is adopted to find the optimum insulation thickness for the common wall structure in Iran. Then, the Iranian standard (Chapter 19) for building envelopes have been reviewed and compared with the minimum requirements of the ASHRAE standard 90.2. The comparison shows that the Iranian standards for buildings envelopes are comparable with the ASHRAE standard 90.2 for the external wall, while ASHRAE requires higher minimum thermal resistance for ceiling compare to chapter 19. The optimization results suggest smaller minimum thermal resistance compare to chapter 19; this is mainly resulting from the characteristic of the Iranian economy (high inflation rate) and subsidized energy sector. Therefore, energy policy in the country needs to be reformed to promote energy conservation in buildings and hence zero energy buildings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".