Nudging window use behaviour through algorithmic setpoint adjustments
Bibliographic record
Abstract
• Building energy inefficiencies exist due to inappropriate use of operable windows. • Behaviour-nudging algorithms can nudge occupants to optimize window use behaviour. • Behaviour-nudging algorithms can reduce heating energy consumption by 35 %. • Survey shows occupant are not dissatisfied with behaviour-nudging algorithms. Mixed-mode buildings combine natural ventilation and mechanical ventilation, which can enhance occupant comfort and reduce their energy consumption. While mixed-mode buildings are common in Canada, inappropriate usage of operable windows by occupants can result in unnecessary energy use. Therefore, regulating occupants’ window use may be necessary to ensure energy efficiency. Previous research demonstrates that the implementation of behaviour-nudging algorithms has the potential to optimize window use. Building performance simulations (BPS) conducted in the past have shown that implementing occupant-centric control (OCC) algorithms can yield energy reductions in mixed-mode buildings. In this paper, field tests have been conducted in a living-lab facility in Ottawa, Ontario with 24 offices to verify simulation results. OCC algorithms applied temporary temperature setpoint overrides in both the summer and winter to nudge occupants to close windows when natural ventilation was not suitable. The algorithms successfully nudged occupants to close windows when natural ventilation was not ideal and reduced the heating energy by approximately 35 %. It was observed that when occupants were nudged to close windows, they tended to keep them closed, highlighting the need for an updated intervention that nudges occupants to open windows when appropriate. Moreover, a survey distributed in the summer of 2024 revealed that occupants in the case-study building reported overall higher satisfaction with the indoor environment compared to occupants in the control building. This suggests that the implemented intervention did not negatively impact occupant comfort, demonstrating the potential of OCC algorithms to optimize energy efficiency and occupant satisfaction.
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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 | 0.000 |
| 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 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".