Estimating Combined Impact of Urban Heat Island Effect and Climate Change on Cooling Requirements of Tall Residential Buildings in Hot-Humid Locations
Bibliographic record
Abstract
Abstract Climate change estimates are critical in developing long-term solutions to the dwelling problems that we currently face. This study combines the impact of climate change and the urban heat island effect to study the outcomes of future weather conditions on the cooling of tall residential buildings in hot and humid climates. For the year 2050, we calculate the impact of urban characteristics through the urban weather generator and climate change through the world weather gen tool on the micro-climatic condition of a district in a newly constructed city near Doha, Qatar, the Lusail City. A total of four weather files are compared to the weather data gathered from the established weather station in the city (two for the year 2020 and three for the year 2050). Results reveal that once the open weather map file has been processed through the urban weather generator (UWG) first and then the climate change model, the MAE increases to 3.30, and the RMSE goes to 3.8 with a maximum deviation of 11.4°c occurring. If the process is done the other way around, the climate change model is applied first, and then the UWG file is applied, the MAE of 3.46 is with RMSE of 3.94 with a maximum deviation of 11.3°c occurring. The impact of these weather files is then assessed on a tall residential building in Lusail. A significant increase of 777197 kwh or 20% is seen in the openweather map file that has been processed first through the climate change model and then through the urban weather generator (as compared to the rural weather file); an increase of 739983 kwh or 19% is seen in the openweather map file that has been processed first through the UWG and then through the climate change model; finally close to 22.6 percent increase or 874088 kwh is seen in the openweather map file that has been processed first through the climate change model and then through the climate change model.
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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 | 0.000 | 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) | 0.001 | 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".