The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?
Bibliographic record
Abstract
Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".