Multizone modelling of a hybrid ventilated high-rise building based on full-scale measurements for predictive control
Bibliographic record
Abstract
Hybrid ventilation is an effective approach to reduce cooling energy consumption by combining natural and mechanical ventilation. Previous studies of full-scale whole-building measurements of high-rise hybrid ventilation are quite limited due to the complexities of buildings and variable ambient conditions. As a result, validated and accurate whole-building simulations of hybrid ventilation often cannot be found in the literature. This paper reports a series of full-scale measurements of hybrid ventilation in a 17-storey high-rise building and associated whole-building simulations by 15-zone detailed and a 5-zone simplified multizone models. The paper is one of the first studies of using multizone models and real-world full-scale data and sharing key operational and performance experience and case studies of high-rise hybrid ventilation. Both the test data and the validated simulation models can be used for the comparison and validation of simulation models. The 5-zone simplified model developed from this study was able to model such a complex high-rise building by only a few zones, making possible the on-line model predictive control of a high-rise building. This was illustrated in this paper by an example of optimizing the uniformity of the hybrid ventilation on different floors by modifying inlet areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".