Thermal Comfort and Energy Analysis of Fan Coil Unit Cooling Systems in Tropical Buildings
Bibliographic record
Abstract
Overcooling is a frequent occurrence in Malaysian buildings, particularly in libraries and classrooms. This study evaluates the thermal comfort of a fan coil unit (FCU) installed in a case study room in Malaysia’s hot and humid climate to detect the problem. On the basis of measurement factors such as temperature, humidity, and airflow, a data acquisition system consisting of sensors and a data logger was selected and installed in the case study room. The measurement data were collected from 8:00 a.m. to 5:00 p.m. for the duration of one week. The cooling load distribution of the case study room was detected from measurement data analysis. 52% of the overall cooling load (9.4 kW) was related to latent loads and 48% to sensible loads. Measuring results indicate that the seminar room’s temperature and relative humidity under the FCU application were 24.3 °C and 77.1%, respectively, values that did not correspond to the thermal comfort level (25 °C and 50% humidity). As a result, it was determined that if room humidity is set to 50%, the room temperature will reach 17 °C, leading to the space being over-cooled. An optimised FCU has been built to achieve the ideal interior atmosphere. Compared to the current FCU, the improved FCU consumes 1.40 times more energy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".