Residential HVAC runtime from smart thermostats: characterization, comparison, and impacts
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
In North America, the majority of homes use forced-air systems for heating and cooling. The proportion of time these systems operate, or runtime, has a significant impact on many building performance parameters. The recent adoption of smart thermostats in many North American homes presents a potential data source for runtime. Smart thermostat data collected from over 7000 homes were compared with nine other investigations and a runtime estimation method based on exterior temperature. The smart thermostat runtimes have a median of 18% across all homes, but show considerable variation between homes, even at constant exterior temperature conditions suggesting that factors besides climate (eg, system sizing, user operation) have a significant impact on runtime. Results from other investigations suggest that smart thermostat runtimes are consistent with other measurement approaches. The practical implications of runtime include the impact on central filtration performance. At low to average runtimes, the filter efficiency matters much less for effectiveness because the system does not run enough for a sufficient air volume to pass through the filter and have a substantial impact on particle concentrations. This work illustrates the importance of measuring runtime for a particular home, and the value of data obtained from smart thermostats.
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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