Investigating thermostat setpoint preferences in Canadian households
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
Occupants' thermostat setpoint preferences play a vital role in HVAC systems' operation and significantly influence the building energy performance. However, despite the diversity in indoor temperature preferences, most building energy codes assume identical thermostat setpoints for buildings of the same type. To this end, this study aims to demonstrate the variations in temperature setpoint preferences across Canadian households by analysing thermostat data collected from ~13,000 residential buildings. The objectives of this study are to (1) determine the average heating, and cooling thermostat setpoints in residential buildings, (2) rank the importance of different attributes that influence setpoint preferences, and (3) extract distinct heating and cooling setpoint profiles. Statistical methods were used to identify the average thermostat setpoints in different provinces. A random forest ensemble learning model was then used to rank the relative importance of different attributes on the setpoint temperatures. Finally, the k-Shape clustering technique was used to extract distinct heating and cooling setpoint temperature profiles. The obtained results were compared with the building energy codes, standards and differences up to ~3°C were found relative to code assumptions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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