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Record W4319588466 · doi:10.1115/imece2022-94272

Estimating Combined Impact of Urban Heat Island Effect and Climate Change on Cooling Requirements of Tall Residential Buildings in Hot-Humid Locations

2022· article· en· W4319588466 on OpenAlexaff
Athar Kamal, Ibrahim Hassan, Liangzhou Wang, Mohammad Azizur Rahman

Bibliographic record

VenueVolume 8: Fluids Engineering; Heat Transfer and Thermal Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsConcordia University
Fundersnot available
KeywordsClimate changeEnvironmental scienceUrban heat islandMeteorologyHot weatherClimatologyWeather stationGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.208
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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