Understanding the role of moisture recovery in indoor humidity: An analytical study for a Norwegian single-family house during heating season
Bibliographic record
Abstract
Providing a high-quality indoor environment with appropriate indoor humidity levels for residential buildings is essential for good physical and mental health, occupant comfort, and long-term building performance. The role of moisture recovery in indoor humidity levels in cold climates has long been the subject of controversy; scholars have debated whether it ameliorates the problem of "too dry" air or causes a new problem of "too humid" air. The current study examines a method using moisture balance equations integrated with moisture recovery to analyse moisture recovery's effect in cold climates. A virtual single-family house in Oslo, Norway, was used to demonstrate the impact of moisture recovery on humidity levels in the kitchen, bathroom, bedroom and living room. The results show that moisture recovery has varying influences on indoor humidity depending on the intensity of moisture recovery, moisture production and ventilation. The indoor moisture production and humidity levels were validated against large-scale field measurements in residential buildings. For the virtual single-family house, the optimal moisture recovery effectiveness is about 50–60% with a 2-min interval, as the "too dry" air (RH<20%) issue is eliminated while the risk of "too humid" air (RH>80%) is not exacerbated. This work also identifies the possibility of controlling or optimising indoor humidity by altering the energy recovery system's moisture recovery effectiveness. Furthermore, the study's findings can be used to optimise thermal comfort or assess epidemiological risk in terms of the impact of indoor humidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".