Developing a weighting scheme for building operational performance: A case study from the Netherlands
Bibliographic record
Abstract
• A novel ontology for buildings’ stakeholder analysis is developed. • This study proposes a weighting scheme for buildings’ operational performance. • Occupants’ needs are considered the most important during building operation. • Individual variations in performance priorities are significant. • Results inform on adapting SRI weighting scheme to regional contexts. Building operations must balance the often-competing performance objectives of occupants’ needs, energy efficiency, and power grid demand, posing a complex multi-criteria decision-making problem. Tailored weighting schemes offer practical approaches to synthesize these diverse performance aspects onto a common scale, effectively informing building operations and overall performance evaluations. However, such schemes remain underdeveloped. To address this gap, a stakeholder ontology was first developed to analyze power dynamics and information flows among stakeholders involved in building operational performance. Building managers were identified as key stakeholders to determine weights of such a scheme. Subsequently, building on the Smart Readiness Indicator (SRI) framework, a survey was conducted to calculate the weights for commercial buildings using the Analytic Hierarchy Process (AHP). Inputs from 27 building managers in the Netherlands were collected, along with 13 building owners and 13 international researchers. Results showed that performance aspects were not equally weighted. Building managers prioritized occupants’ health and wellbeing, building service downtime, and occupant comfort, while assigning the lowest weight to operational cost. Building owners and researchers also agreed with these top three priorities. However, considerable individual variation in priorities was observed, even after accounting for stakeholder roles, building types, and country. These findings suggest that the SRI weighting scheme should be adapted to regional contexts and highlight the necessity for customizable building management dashboards tailored to specific building conditions. Finally, the proposed weighting scheme offers pragmatic insights to support decision making in building operations, policy development, certification systems development, and smart building control and management.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".