An Inverse Model-based Approach to Estimate Air Infiltration in Commercial Buildings using Occupant-generated CO2 and Humidity
Bibliographic record
Abstract
Air infiltration has a significant impact on building energy performance and the indoor environment.Monitoring air infiltration continuously is of great importance to compare the airtightness of a building over time, and to detect building envelope degradation over time.An accurate estimate of air infiltration rate also informs envelope retrofit decisions to improve airtightness.However, air mobility and other environmental factors, such as wind or indooroutdoor temperature differences, often make the accurate measurement of air infiltration challenging.Further, conventional air infiltration testing approaches such as fan pressurization and tracer gas tests possess certain drawbacks limiting their applicability in commercial buildings.To address the limitations of air infiltration tests, this research proposes a low-cost inverse modelbased approach for estimating air infiltration rates by extracting the occupant-generated carbon dioxide (CO2) and humidity data from a building automation system (BAS).The laboratory tracer gas concentration decay tests were carried out to explore the effectiveness of replacing sulphur hexafluoride (SF6) with CO2 and the appropriateness of using low-cost BAS-grade sensors.Then the applicability of the proposed inverse model-based approach was verified by tracer gas concentration decay tests using both CO2 and water vapour.In this case, the historical CO2 and humidity data were used to validate the model to examine whether this approach can estimate infiltration rates from historical data.At last, return air CO2 concentration data from three air handling units were utilized to demonstrate this novel approach.Different regression models were developed to investigate the suitability of this ubiquitous sensor type to estimate building-level infiltration rates.The results indicated that the proposed method could conveniently lend itself to estimate air infiltration rates at a reasonable accuracy using existing sensor data.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".