Roof-Integrated Green Technologies, Energy Saving and Outdoor Thermal Comfort: Insights from a Case Study in Urban Environment
Bibliographic record
Abstract
Green urban infrastructures have a significant impact on urban climate mitigation, on indoor and outdoor thermal comfort and on energy performance of buildings. In this paper, outdoor thermal comfort conditions and energy saving for space heating and cooling were investigated before and after the use of roof-integrated green technologies. Existing urban energy and climate models and tools were applied to an urban area located in a Turin (Italy). CitySim, ENVI-met and SOLWEIG tools and a GIS-based model were used to evaluate the mean radiant temperature and the thermal comfort of outdoor spaces before and after the use of vegetated roofs and green surfaces such as the predicted mean vote (PMV), the physiological equivalent temperature (PET) and the universal thermal climate index (UTCI). A GIS-based engineering model and CitySim tool were used to evaluate the energy saving and energy independence index for space heating and cooling after the use of green roofs and solar technologies. According to the shape and the suitability of rooftop elaborated with GIS tools, some roofs were identified as potential green roofs other as potential solar roofs for installing solar thermal collectors and photovoltaic panels. According to the results it is possible to confirm that the use of green roofs and urban greenery can decrease the mean radiant temperature until about 10℃ during summer season, improving outdoor thermal comfort conditions and energy savings with a reduction of 12% for space cooling energy consumption.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".