Improving thermal comfort conditions in K-12 educational buildings in hot and humid climate: a case study in Cucuta, Colombia
Bibliographic record
Abstract
Abstract The school buildings in Colombia are built based on geographical locations and regional construction systems. However, external weather conditions and building design can have a significant impact on the thermal comfort of students, which affects the academic performance and productivity. This paper investigates the thermal comfort performance for an educational building in a hot and humid city in Cucuta, Colombia, built under national guidelines. This school is a concrete structure without mechanical cooling. However, field observation discovered that 82% of the time students experienced thermal discomfort. To investigate causes and provide mitigation strategies, a whole building energy simulation is conducted. Design Builder is used to evaluate the indoor thermal conditions compared to outdoor data collected. ASHRAE 55 adaptative model is used for the evaluation. It is found that 79% of the time the thermal conditions are outside the acceptable range during the year. The effect of mitigation measures i.e., occupancy, roof insulation, and natural ventilation rates are investigated through simulations. It is found that occupancy and natural ventilation rate have a significant impact on the indoor temperature and relative humidity, and thus the thermal comfort. Passive design strategies are proposed in optimizing the school building design to meet ASHARE-55 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".