Impact of temperature and moisture dependent conductivity of building insulation materials on estimating heating and cooling load using typical and historical weather data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper investigates the impact on heating and cooling load estimation when effective thermal conductivity of materials is incorporated into building energy simulation in conjunction with historical weather data. Under current practice, thermal performance of building envelope systems is usually represented by a lumped nominal conductivity value. In reality, effective conductivity is influenced by many factors such as temperature and moisture content. To minimize computing time, building energy simulation is also conducted with typical meteorological weather data, which is sufficient in estimating average energy use of buildings, but lacks the ability to truly reflect building performance under long term weather conditions. Preliminary research has been conducted by simulating a typical residential house in Toronto using WUFI plus - a comprehensive hygrothermal building simulation program. Historical CWEED - 1998 to 2014 weather data and typical weather files CWEC-1990s and 2016 have been used for this work. The results shows:1)a reduced representativeness of typical weather data in building energy simulation as climate changes over time; 2)estimation using typical weather data is not representative of any individual historical year and 3)performance of insulation materials changes when temperature and moisture dependant conductivity is considered and affects the results of building energy simulation.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it