Data-driven identification of occupant-thermostat interactions in small commercial buildings
Bibliographic record
Abstract
Small commercial buildings owners and utility managers often look for opportunities for energy and greenhouse gas emission savings through various energy efficiency approaches. However, in Canada, the small commercial buildings are currently underserved by energy conservation tools because of their dispersion and lower payback potential. Connected thermostats provide a low-cost solution to collect massive amounts of data from a portfolio of these buildings that can be used to improve the understanding of their energy use behaviors. Similar to their residential application counterparts, these thermostats can be overridden by building occupants and potentially hinder more advanced controls or coordination across a managed building portfolio. In this work, we investigated the temperature setpoint overrides from more than 620 thermostats across 250 small commercial buildings located in Ontario, Canada. In particular, we developed a global model to estimate the fraction of buildings in the portfolio that experienced overrides each hour. We were able to correctly predict the percentage of overridden buildings within 2%. Such a predictive model may help the portfolio manager to forecast future conditions in order to create more efficient energy saving and peak reduction programs without compromising the occupants' thermal comfort and organizational productivity.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".