Energy Impacts of Envelope Tightening and Mechanical Ventilation for the U.S. Residential Sector
Bibliographic record
Abstract
Effective residential envelope air sealing reduces infiltration and associated energy costs for thermal conditioning, yet often creates a need for mechanical ventilation to protect indoor air quality. This study estimated the potential energy savings of implementing airtightness improvements or absolute standards along with mechanical ventilation throughout the U.S. housing stock. We used a physics-based modeling framework to simulate the impact of envelope tightening, providing mechanical ventilation as needed. There are 113 million homes in the US. We calculated the change in energy demand for each home in a nationally representative sample of 50,000 virtual homes developed from the 2009 Residential Energy Consumption Survey. Ventilation was provided as required by 2010 and proposed 2013 versions of ASHRAE Standard 62.2. Ensuring that all current homes comply with 62.2-2010 would increase residential site energy demand by 0.07 quads (0.07 exajoules (EJ)) annually. Improving airtightness of all homes at current average retrofit performance levels would decrease demand by 0.7 quads (0.74 EJ) annually and upgrading all homes to be as airtight as the top 10% of similar homes would double the savings, leading to roughly $22 billion in annual savings in energy bills. We also analyzed the potential benefits of bringing the entire stock to airtightness specifications of IECC 2012, Canada's R2000, and Passive House standards.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".