Impact of influential factors on gray-box model performance for identification of building thermal properties: numerical and analytical analyses
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
A full-blown global energy crisis and intensive global warming have made it urgent to develop sustainable buildings, which is a significant contributor to energy consumption and related greenhouse gas emissions. How to prioritize existing buildings’ retrofit plays a key role in the sustainability process. Thanks to the development of sensor techniques and data engineering, thermostat data has become more popular due to its easier acquisition. Researchers have explored the feasibility and performance of using thermostat data as an alternative to energy data, especially in building retrofit estimation, and the results are promising. This paper investigates the impact of influential factors of Newton’s law of cooling on the reliability and accuracy of its application to identification of building thermal properties. The authors quantified the influential factors impact. Both numerical and analytical analysis are conducted. In addition, the impact of building’s complexity on the estimation performance is investigated. The authors also propose the conception of time constant intensity for prioritization of buildings’ retrofit instead of conventional time constant.
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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 | 0.001 |
| 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) | 0.000 | 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