A Comparison of Empirical Indoor Relative Humidity Models with Measured Data
Bibliographic record
Abstract
The focus of this study is to examine the reliability of models that are available in the open literature for simulating the interior moisture conditions, comparing the predicted interior relative humidity (RH) to measured data. Four models, for predicting the indoor RH in houses are tested against measured RH data for 25 houses. The models considered are primarily developed as design tools. The models tested are the European Indoor Class Model, the BRE model, and the ASHRAE 160P simple and intermediate models. The RH in each house is measured in two different locations producing 50 data sets. The ASHRAE intermediate model seemed to be the most robust exhibiting lower errors when compared to measured data. The European Indoor Class also performed well and can be used when data regarding moisture generation and/or air change rates is not available. As a design tool, however, it is not universally conservative in estimating the indoor RH. The BRE is problematic and generally exhibits large positive errors for most of the houses surveyed. It is found to be not reliable for the North American houses investigated in the comparisons. The ASHRAE simple model also exhibited large positive errors and does not trend well with the measured conditions. Models that greatly overestimate the design loads should be used with caution as they may lead to complicated inefficient designs.
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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 | 0.005 | 0.022 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".