Reduction of HVAC system runtime due to occupancy-controlled smart thermostats in contemporary multi-unit residential building suites
Bibliographic record
Abstract
Abstract Previous studies in single family homes have demonstrated a reduction in space conditioning energy demand through the use of occupancy-controlled smart thermostats. This technology has the potential to reduce space conditioning demand in multi-unit residential buildings (MURBs) as well, however no previous studies have tested the performance of smart thermostats in this building type. Field data were collected from 56 thermostats installed in two condominium buildings located in Toronto, Canada. Thermostats installed in each suite were operated using through three different control scenarios during the monitoring period: 1) a baseline scenario, where the thermostat is operated as a standard programmable thermostat, 2) an occupancy-based control scenario, and 3) a load-shifting control scenario. Baseline runtime data collected while the thermostats were operated as a standard programmable thermostat was used in combination with weather data and a supervised learning regression algorithm (Random Forest) to estimate the baseline runtime for each suite on days that the occupancy-based control strategy was running. When the estimated baseline runtime derived from the regression model was compared with the actual system runtime while the occupancy-based control strategy is running, an average reduction in suite HVAC system runtime of 17% was found.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".