Developing a residential occupancy schedule generator based on smart thermostat data
Bibliographic record
Abstract
Occupancy patterns play a major role in residential buildings’ energy demand. This role becomes essential to represent realistically in urban-scale energy simulations with the focus on matching the supply of renewable energy to the demand of different sectors. However, the lack of large-scale datasets that represent the seasonality and dynamic of occupancy schedules, especially for the residential sector limited such analysis. Recently, the fast adoption of smart thermostats, featuring passive infrared sensors for motion detection, in residential buildings has allowed for the development of more representative occupancy schedules for different applications. To this end, this study introduces an open-source Python package to generate large-scale hourly occupancy profiles for residential buildings based on smart thermostat readings. The package takes advantage of the Donate Your Data (DYD) dataset by Ecobee to develop a rule-based framework that addresses the limitations of relying on motion-detection data to represent the whole-building occupancy. The framework was applied to over 8,000 Canadian households as a case study. The generated profiles for these buildings are validated by comparing them with residential occupancy profiles generated from the Canadian Time Use Survey (TUS). The results showed that both profiles were statistically similar with a 3% difference in the aggregated daily occupied hours. Finally, the diversity of the generated profiles before and after the COVID-19 pandemic is investigated to demonstrate the usefulness of the tool. The results proved the potential of the developed package to generate realistic and diverse occupancy schedules for the residential sector.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".