Analyzing smart thermostat data to uncover trends in remote work behaviors
Bibliographic record
Abstract
• After COVID-19 experience, home occupancy hours have increased in many households. • Teleworkers use more energy-intensive setpoints in their homes than when they are away. • Teleworkers are twice as likely as non-teleworkers to purchase an AC for their home. • Building codes need re-evaluation to address increased home occupancy hours. The widespread adoption of hybrid work arrangements due to COVID-19 pandemic warrants an examination of post-pandemic home energy consumption. There is considerable uncertainty regarding teleworkers’ energy-related behaviors when they telework. This study investigates teleworkers’ thermostat use preferences when working from home using data from the “ecobee” thermostat “Donate Your Data” program. The dataset included occupancy and setpoint data from 3,789 houses with records both before and after the pandemic, enabling us to estimate teleworkers’ setpoint usage across Canada. To identify households with teleworker(s), we analyzed weekly house occupancy patterns, and tracked the number of hours each home was vacant during regular office hours. The study reveals that 12 % of the 3,789 analyzed households in Canada adopted telework post-pandemic, with 31 % working from home five days a week. Among the 472 households adopting telework, 42 % used setback strategies during winter weekdays pre-pandemic. The findings indicated that households engaged in teleworking raised their winter thermostat setpoints by an average of 0.66 °C and decreased their summer thermostat setpoint by an average of 1.62 °C compared to the period that their home was vacant during office hours. Additionally, 25 % of households without air conditioners before the pandemic purchased one afterward when they adopted remote work, in comparison to only 11 % of non-teleworking households without an air conditioner purchased one after the pandemic. These findings highlight the changing patterns of occupancy and thermostat preferences among Canadian households in the post-pandemic era, aiding researchers in future energy quantification related to remote work adoption.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".