An Extensive Study of the Urban Heat Island Phenomenon in Rome, Italy: Implications for Building Energy Performance Through Data from Multiple Meteorological Stations
Bibliographic record
Abstract
Urban areas can be characterized by higher outdoor air temperatures than rural ones due to the well-known Urban Heat Island (UHI) phenomenon.This significantly affects buildings energy performance, thus influencing energy needs in terms of cooling and heating.Starting from this, the UHI in Rome was here investigated, providing an updated estimation through 2022 climate data.The influence of the UHI on energy efficiency in buildings has been highlighted by applying climatic data logged by different weather stations within a dynamic simulation tool.Thus, actual climate data have been used as thermal boundary conditions to simulate typical building energy needs, for cooling and heating.The findings of this study demonstrate a notable disparity in climatic conditions between the areas outside the city and the urban context.The maximum values of UHI intensities for daytime and night-time were identified as 3.1℃ and 3.5℃, respectively.Moreover, the accurate selection of reference data is a crucial factor for obtaining reliable information regarding the energy demands of buildings within the city.The findings emphasized that utilizing data from airport stations instead of weather stations within urban areas can result in disparities up to approximately -17% for heating and more than 50% for cooling requirements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".