Asymmetries in global building envelope air infiltration and tightness data: Exploring knowledge gaps for accurate energy analyses
Bibliographic record
Abstract
Air infiltration is one of the leading causes of energy loss and heat gain in indoor building spaces. It significantly burdens energy use for heating and cooling, affects indoor comfort and air quality, and increases CO2 emissions, intensifying climate change. While extensive field test results have been reported in the literature, this paper aims to investigate the comprehensiveness of this published data. The research synthesizes published field test data from more than 60 sources to provide benchmark infiltration values for different building typologies, construction methods, and climate zones. Additionally, climate data estimates the average energy burden of infiltration across various climate zones. The analysis highlights critical gaps in envelope infiltration data collection for non-residential buildings and significant gaps in geographic regions outside Europe, the U.S.A., and Canada. This paper presents three main guiding questions addressing key research gaps related to air infiltration in buildings and its impact on energy performance and indoor comfort. The proposed questions and answers aim to build upon existing knowledge, provide valuable insights for ongoing and future research, and inspire exploration into innovative strategies for improving building energy efficiency and occupant well-being. It also provides a list of actionable recommendations for air infiltration testing and research to address the now-apparent gaps. • The research is a comprehensive systematic study of air infiltration knowledge across building typologies and climate zones. • Field measurements are synthesized to find trends and gaps in the currently available data. • This study investigates how air infiltration affects energy consumption and building performance simulations. • This research helps integration of field measurements into energy simulation models to enhance accuracy and reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".