Optimizing Indoor Air Quality with Multi-Zone Modeling: Comparative Calibration Using Ensemble Kalman Filter & Genetic Algorithm
Bibliographic record
Abstract
Indoor air quality (IAQ) modeling is essential for evaluating and optimizing modern buildings' ventilation systems, pollutant dispersion, and occupant health. Accurate IAQ predictions rely on calibrated models that effectively represent real-world conditions. This study focuses on calibrating the CONTAM multi-zone airflow model using two advanced methodologies: the Ensemble Kalman Filter (ENKF) and the Genetic Algorithm (GA). To improve the model's predictive accuracy, calibration efforts targeted key parameters, including initial CO₂ concentrations, generation rates, and occupancy counts. These methods were validated using CO₂ tracer gas experiments conducted across multiple test scenarios, including room-to-floor and floor-to-floor dispersion dynamics. Results demonstrate that ENKF calibration achieved RMSE reductions of approximately 25% in complex multi-zone airflow scenarios, with CVRMSE consistently below the ASHRAE-recommended threshold of 15%. The GA method performed similarly in accuracy but required higher computational resources, making it more suitable for static or offline calibration processes. The calibrated models were subsequently applied in a case study to analyze CO₂ quanta dispersion in an experimental building with controlled ventilation strategies. This study investigated the effects of varying fresh air percentages (10% to 100%) and stairwell pressurization on contaminant transport. Results from the calibrated models revealed critical insights, such as the effectiveness of high fresh air intake in mitigating pollutant concentrations and the impact of pressurization strategies on inter-zone airflow. These findings highlight the practical value of calibration in refining IAQ predictions and informing operational strategies. This research underscores the importance of hybrid calibration techniques for improving the reliability of multi-zone IAQ models and their application in dynamic building environments. By integrating calibrated models into operational planning, stakeholders can optimize ventilation performance, reduce energy consumption, and enhance occupant safety in diverse building types.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".