Using smart thermostat override data to provide insights for improving heating, ventilation, and air-conditioning system scheduling in a portfolio of small commercial 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
Managers of small commercial building (SCB) portfolios need to understand occupant interactions with heating, ventilation, and air-conditioning (HVAC) systems to reduce energy use and greenhouse gas (GHG) emissions. In Canada, SCBs are currently underserved by energy conservation and thermal analysis tools because of their dispersion and lower payback potential. However, the emergence of smart thermostats (STs) and their central data collection platform provide a cost-effective solution to gather data from portfolios of SCBs and improve our understanding of occupant-HVAC interactions. This article analyzes the relationship between HVAC schedules (temperature set-points), indoor thermal conditions (dry-bulb temperature and relative humidity), and occupant behavior (thermostat overrides) in a portfolio of 30 SCBs in Ontario, Canada. The results reveal that temperature set-points were not properly selected in the portfolio of SCBs, leading to a large range of indoor thermal conditions and increased thermostat overrides. Specifically, the study demonstrates that building- and zone-specific HVAC schedules are necessary to minimize discomfort and reduce energy consumption in the portfolio of SCBs. The findings of this study can provide valuable insights for portfolio managers to improve HVAC schedules in a manner that reduces energy consumption and GHG emissions while accommodating occupants’ thermal comfort and productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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