Enhancing occupant window use through behaviour nudging algorithms
Bibliographic record
Abstract
Abstract Occupants spend a significant amount of their time indoors, and as a result, their comfort is an important consideration. 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 heating demand. The study involved two phases of nudging: the Setback-Only phase and Backlight-Only phase, which were each compared with one another and against previously collected historical data. The study found that both interventions substantially reduced window opening durations when natural ventilation was not ideal, with the Setback-Only intervention being the most effective. The study also determined that heating demand was reduced by approximately 40% due to the Setback-Only intervention and by approximately 6.6% due to the Backlight-Only intervention. These findings highlight the potential of behaviour nudging algorithms on occupant behaviour and on reducing heating demand, with the Setback-Only intervention showing the greatest impact.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".