Method for Assessing Thermal and Economic Benefits of Cool Roofs in Unconditioned Buildings
Bibliographic record
Abstract
The use of cool roofs is a passive technique to reduce the cooling load of buildings in hot climates, moreover, it is use has the potential to mitigate the heat island phenomenon. This study presents a method to quantify and monetize the benefits of applying the cool roof passive technique in buildings without air conditioning. The proposed method can be adapted to other passive techniques. For this study, an experimental step was carried out to measure the thermal emittance and reflectance of fiber cement tile samples. The study also presents a thermoenergetic performance analysis using EnergyPlus simulations. The last part highlights the innovation of this method in relation to other studies, which consists of monetizing the thermal benefits of the cool roof passive technique. The analysis contemplates a single-family residential building model for three cities in different regions of Brazil (Florianópolis/SC, São Paulo/SP and Manaus/AM). Fiber cement tiles in their natural color were considered and painted white, representing a cool roof. The solar reflectance of the white-painted samples was around 50% higher than that of the conventional (unpainted) samples. When comparing conventional and cool roofs, the reduction in heat flow through the roof was over 80 % for all three cities. Although the building analyzed does not have air conditioning, the proposed method makes it possible to quantify the thermal load that the passive technique could avoid. The most significant results were in Manaus, resulting in a possible saving of US 4.2/m² per year.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".