The importance of understanding occupant behaviour in buildings: exploring the nexus of design, management, behaviour, and energy use in green, high-rise residential buildings
Bibliographic record
Abstract
The quest to ‘green’ the built environment has been ongoing since the early 1970s and has intensified as the threat of exceeding 450 ppm of atmospheric carbon dioxide has become more real. As a result of this, many contemporary residential high-rise buildings are designed with hopes of achieving carbon emission reductions, while not sacrificing occupant satisfaction, or property value. Little is known about how the occupants of these buildings contribute to the energy and water consumed therein, nor the effects that these design aspirations have on occupant satisfaction. The present study relies on data collected in four recently built, Leadership in Energy and Environmental Design [LEED] certified, high-rise, residential buildings in Ontario, Canada. Using various sources of data (i.e., from energy and water submeters, questionnaire responses, interviews, and physical data relating to each suite) the extent to which physical, behavioural, and demographic variables explain suite-level energy and water consumption was explored. Energy use intensity differed by a factor of 7 between similar suites, electricity by a factor of 5, hot water by a factor of 13, cooling by a factor of 47, and heating by a factor of 67. Results show that physical building characteristics explain 43% of the heating variability, 16% of the cooling variability, and 40% of electricity variability, suggesting that the remainders could be a result of occupant behaviour and demographics. It was also discovered that 52% of respondents were not using their energy recovery ventilators [ERV] for the following reasons: acoustic dissatisfaction, difficulty with accessibility of filters, occupant knowledge and preferences, and a lack of engagement with training materials. Results suggest that abandoning mechanical ventilation in favour of passive ventilation could actually lead to greater satisfaction with indoor air quality and to decreased energy consumption. Using content analysis of questionnaire comments, the utility of contextual factors in understanding energy use and satisfaction in the study buildings, as well as their value in producing feedback for designers and managers, was explored. Combining quantitative and qualitative datasets was an effective approach to understanding energy use in this understudied building type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".