INFLUENCING FACTORS ON COOLING DEMAND OF HIGH-RISE BUILDINGS IN HOT/HUMID CLIMATES: A REVIEW
Bibliographic record
Abstract
A significant amount of energy is consumed on cooling buildings in countries that experience hot/humid climates. The increasing demand for high-rise buildings, with their inherent air conditioning systems, adds extra requirements to electricity grids or local district cooling systems. Thus, this work is structured to identify the influencing factors of cooling energy demand in high-rise buildings that are geographically restricted to countries of these climates. The influence of the factor is quantified as its contribution to cooling energy savings when manipulated or optimized. It is found that the average annual cooling reductions are 12%, 24.7%, 18.3%, and 20% with ranges of 3%-27%, 2.6%-60%, 5.6%-30%, and 11%-29% for building typology, envelope, system, and operation factors, respectively. Environmental factors lack quantification in the literature, although they are considered, however their effect is not quantified. In general, most studies considered building typology and building envelope factors which are related to building design, while few studies considered building operation and building system factors. The aforementioned factors and their importance lead to suggestions of conducting more studies on building operational and building system factors as they significantly contribute in cooling energy savings. Since Urban Heat Island (UHI) can cause a change of a city's microclimate which may double the cooling demand, it is listed as one of the essential environmental factors. This review has shown various aspects that are vital in studying building cooling load demand and its related energy performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".