Influencing occupant window use through behaviour-nudging algorithms
Bibliographic record
Abstract
• Inappropriate operation of windows contributes to building energy inefficiencies • Adjusting temperature setpoints is more influential than relying on visual cues • Behaviour-nudging algorithms can reduce heating and cooling energy demand • Survey results suggest behaviour-nudging algorithms did not negatively impact occupant comfort Operable windows are essential as they can provide natural ventilation, reduce the need for mechanical ventilation, and improve occupant comfort. However, window use needs to be regulated to avoid unnecessary energy consumption. This paper presents findings from field tests conducted in a living-lab facility with 24 private offices to explore the potential benefits of implementing nudging signals to adjust occupants’ window use behaviour and energy demand. The study involved three phases of nudging: Setback and Keep (SK), where the temperature setpoint was adjusted and maintained (kept) until the window closed and the occupant intervened; Setback and Revert with Backlight (SB), where the temperature setpoint was adjusted and reverted immediately to the occupant’s preferred setpoint once the window was closed; and Backlight-Only (BO) phase, which were all compared with each other and against previously collected historical data. The study found that all three interventions reduced window opening durations when natural ventilation was not ideal, with the SK intervention being the most effective during extreme temperatures. The study also determined that heating energy demand was reduced by approximately 38% and 35% after the SK intervention and the SB intervention, respectively, and increased by approximately 4% after the BO intervention. Additionally, the SB intervention realized cooling energy savings of approximately 20%. Furthermore, a survey revealed that the thermostat backlight colours were noticeable and influential. These findings demonstrate that behaviour-nudging algorithms are effective at regulating window use and improving energy performance in mixed-mode buildings.
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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".