Improving the energy efficiency of existing residential buildings by applying passive and cost-effective solutions in the Hot and humid region of Iran
Bibliographic record
Abstract
This paper aims to investigate the improvement of the energy efficiency of typical apartment buildings in the hot and humid region of Iran by applying passive and cost-effective solutions. For this purpose, a residential building, that reflects the current typology, is selected in Bushehr, Iran, and its annual energy consumption is explored using electric and gas bills. Then, a primary and calibrated model of the building is made using a real occupancy schedule and hourly weather data of Bushehr in Design Builder Software to simulate its energy performance. Considering the architectural design of the baseline model and using passive solutions (including low-E glazing, thermal insulation of external walls, roof and ceilings, and pre-heating of domestic hot water), a developed model is made. The simulation results indicate that the annual carbon dioxide emission and energy consumption of the developed model is reduced by 18.7% and 20%, respectively. These passive solutions can be used to improve the energy efficiency of existing buildings to achieve low Carbon buildings and neighborhoods in this part of Iran. This study also proposed a new reference for the annual energy consumption of low-energy houses in the hot and humid area of Iran and the Persian Gulf region (75-76 kWh/m2/Year). Moreover, the economic analysis in this study demonstrates that the above-mentioned passive solutions will be cost-effective if the government subsidies for the residential buildings’ energy use are eliminated in this region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".