Development and testing of an automated tool to leverage building energy models for thermal resilience analysis
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
Unlike building energy use analysis, thermal resilience analysis to understand how buildings perform during disruptive events has not been widely adopted or standardized. To ensure best practices are applied consistently, this paper proposes a new automated toolbox to leverage building performance models developed for energy analysis purposes, for thermal resilience analysis. To demonstrate the functionality and robustness of the toolbox for automating the analysis and reporting of thermal resilience, example simulations are completed with models gathered from different sources. Major contributions from this study include the establishment of a standardized transparent simulation framework for thermal resilience analysis, as well as the proposal of techniques for overcoming challenges associated with a diversity of model sources including different modelling habits/preferences by modellers and different versions of EnergyPlus. Recommended future work includes the development of standardized modelling configurations reflecting extreme events; the inclusion of more comprehensive metrics, and the refining analytics report through consultation with stakeholders.
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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 | 0.001 | 0.001 |
| 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