A Review on Prediction Uncertainty in Exterior Heat Transfer Coefficient-Based Building Thermal Load: A Case of Microclimate
Bibliographic record
Abstract
A correct prediction of building cooling load is essential in building energy consumption in a hot and humid urban area.To this extent, the current study emphasizes a meticulous review of different convective heat transfer coefficient correlations including those developed considering neighbourhood microclimate effect, and the existing ones used in building energy simulation programs such as EnergyPlus, Environmental Systems Performance -Research (ESP-r), Integrated Environmental Solutions Ltd (IES), IDA, and TAS.Furthermore, rigorous quantitative assessment of associated convective thermal load from the windward, leeward, and roof surfaces under the case of microclimatic conditions is performed.The data used in the current assessment are computational fluid dynamics results, as a reference, from previously published data and actual weather data from the hot and humid climate.It is observed that very few convective heat transfer coefficient correlations show closer predicted thermal load (deviation less than 30%) with computational fluid dynamics results, and others exhibit a varying degree of prediction ability with over-predictions in general for the windward, leeward and roof surfaces.Current analysis suggests that further attention is required to increase the prediction ability of convective heat transfer coefficient correlations by developing a convective heat transfer coefficient model considering computational fluid dynamics analysis of the whole district, validating and modifying or redefining existing convective heat transfer coefficient correlations based on real field measurement data considering flow field around the building, and incorporation of urban morphology, vegetation, urban heat island, and urban pollution level in convective heat transfer coefficient correlations development.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".