Modeling window and thermostat use behavior to inform sequences of operation in mixed-mode ventilation buildings
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
Operable windows have become desirable design features of modern mechanically ventilated office buildings in North America. While they improve perceived control and adaptive comfort, their inappropriate use poses risks associated with increased heating and cooling energy use. Therefore, the sequence of operations for terminal devices serving zones with operable windows should be designed in recognition of these risks, which in turn should be informed by research investigating occupants’ window and thermostat use behavior. To this end, this paper examines window and thermostat use data collected from two mixed-mode ventilation buildings in Ottawa, Canada. Discrete-time Markov logistic regression models and decision tree models were established to predict the likelihood of thermostat keypress and window opening/closing instances and identify the indoor conditions that trigger these actions. Based on this analysis, a set of preliminary recommendations is developed to improve terminal device sequencing in mixed-mode ventilation buildings in cold climates such that the comfort and energy savings potential of operable windows can be fully realized. The recommendations include applying thermostat setpoint setback to encourage occupants to open windows when conditions are advantageous for saving energy and discourage occupants from opening windows when energy penalties may be caused.
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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 | 0.000 |
| 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