Overview of occupant-centric KPIs for building performance and their value to various building stakeholders
Bibliographic record
Abstract
Recognizing the significance of occupants and their interactions with the building’s operational systems, various occupant-related key performance indicators (KPIs) have been established. However, there remains a notable gap concerning the practical utilization of these indicators by building stakeholders. Therefore, this study provides a comprehensive overview of the occupant-related building performance KPIs and how various building stakeholders influence them through their decision-making. The study assessed: (1) roles of various building stakeholders; (2) stakeholders’ requirement for occupant-centric KPIs in their decision-making process; (3) factors demonstrating effective occupant-centric KPIs along with data requirements to evaluate them; and (4) stakeholders’ decisions and actions that affect occupant-centric KPIs. Key stakeholders identified involved investors, building owners, designers, building occupants, building managers and operators, as well as utility providers. Effective occupant-centric KPIs were characterised by their fit-for-purpose, actionability, quantifiability, comparability, reproducibility, integrability, feasibility, and usability. Results indicated that building managers and operators primarily affect thermal, air quality, and visual KPIs. Building occupants, public authorities, investors, building owners, and designers influence all categories of occupant-centric KPIs. Utility providers particularly impact thermal KPIs. In conclusion, findings of this study contribute to shaping the way in which stakeholders consider occupants in their decision-making to achieve building performance objectives by data-driven occupant-centric KPIs.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".