The effect of occupant comfort preferences and use patterns on the space heating loads in the context of the next home unit
Bibliographic record
Abstract
The occupant plays an important role in residential energy consumption, and likewise, a critical part in energy conservation. Studies have shown that energy consumption in similar houses can vary significantly due to the occupant’s behaviour and household characteristics. Nevertheless, very few studies have focused on identifying the occupant driven parameters responsible for energy variations or on quantifying their impact. This study analyzes the impact of the occupant’s preferences of temperature settings, the number of thermostats used, the door operations, the use of window curtains, and the fenestration’s effect on the heating loads of a residential unit. This analysis quantifies the impact of each occupant related parameter as a function of various factors, including occupancy patterns, interior layouts, orientation, and volumetric occupation. The research answers the following questions: How significant is the influence of occupant behaviour on space heating loads? How does this vary with changes in occupancy patterns and spatial configurations? How significant is the influence of changes in fenestration on space heating loads relative to occupant behaviour? Seventeen design scenarios are generated, through which the impact of the occupant behaviour and her/his design preferences is evaluated. The results, which are generated using IES VE software, identify the impact of each occupant related parameter on the heating loads using the Next Home Unit as a model house, located in a Toronto climate. In general, the results show that the occupancy patterns, interior layout, and volumetric occupation can significantly change the impact of each occupant related parameter on a unit’s heating loads. On average, reducing a unit’s temperature from 23℃ to 18℃ or installing low e double glazed windows, most significantly reduces the heating loads. The lowest impact on the heating loads is found when changing the fenestration area and leaving the doors open. The study shows that an occupant’s actions -- such as keeping doors close, heating the room only when occupied, reducing the unit’s temperature, and installing low e glazing -- can reduce the energy required for heating. However, as the research shows, the effect of the above actions can significantly vary due to occupancy patterns, interior layout, and volumetric occupation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".