Integrating Urban Heat Island Impact into Building Energy Assessment in a Hot-Arid City
Bibliographic record
Abstract
Dense cities usually experience the urban heat island (UHI) effect, resulting in higher ambient temperatures and increased cooling loads. However, the typical lack of combining climatic variables with building passive design parameters in significant evaluations hinders the consideration of the UHI effect during the building design stage. In that regard, a global sensitivity analysis was conducted to assess the significance of climatic variables and building design features in building energy simulations for an office building. Additionally, this study examines the UHI effect on building energy performance in Qatar, a hot-arid climate, using both measurement data and computational modeling. This study collects measurement data across Qatar and conducts computational fluid dynamics (CFD) simulations; the results from both methods serve as inputs in building energy simulation (BES). The results demonstrate that space cooling demand is more sensitive to ambient temperature than other climatic parameters, building thermal properties, etc. The UHI intensity is high during hot and transition seasons and reaches a maximum of 13 °C. BES results show a 10% increase in cooling energy demand for an office building due to the UHI effect on a hot day. The results of this study enable more informed decision-making during the building design process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".