accim: a Python library for adaptive setpoint temperatures in building performance simulations
Why this work is in the frame
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Bibliographic record
Abstract
Building performance simulations (BPS) can be used to estimate the energy required to deliver indoor environmental conditions acceptable for the occupants. Although the adaptive approach has been historically addressed only to naturally ventilated spaces, recent research has found that it could also be applied to air-conditioning spaces. Thus, it is possible to use setpoint temperatures based on adaptive comfort models as energy-saving measures. This study presents a seamless methodology based on the use of accim, an open-source software tool to automate the use of adaptive setpoint temperatures in building performance simulations. accim allows to use script-based workflows to perform all actions within the development of a simulation-based thermal comfort study. A case study is used to demonstrate the capabilities of accim. The results show that accim provides a wide range of new possibilities for developing studies related to the energy implications of adaptive thermal comfort.
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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 | 0.002 |
| 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