Enhancing Building Performance by Insights into Occupant Behavior through Occupant-Centric Key Performance Indicators
Bibliographic record
Abstract
This article introduces a novel framework for Occupant-Centric Key Performance Indicators (OC KPIs) which aims to enhance building performance by aligning with occupant presence during the operation phase. Unlike traditional occupancy-agnostic KPIs, OC KPIs can better emphasize the usefulness of energy use in the building to provide service and indoor comfort to occupants when they are actually present. Furthermore, OC KPIs have the potential to help detect energy use inefficiencies, savings opportunities, system anomalies/faults, and insufficient Indoor Environmental Quality (IEQ). Within this framework, the study explores 52 traditional and OC KPIs focusing on heating use, electricity use, domestic hot and cold water, thermal comfort, and air quality. The study case is a multi-story residential low-energy building located in Denmark (five apartments, 16 rooms). The findings suggest that incorporating occupancy data in the OC KPI calculations enables a deeper understanding of energy-related occupant behavior and IEQ, offering building managers and occupants insights. These include 1) the potential for installing a more advanced heating control algorithm by analyzing occupancy over time in relation to heating use, and 2) the identification of significant appliance-related behavior in correspondence with load matching, which can be used for predictive maintenance (e.g., degradation) or personalized energy use feedback. In addition to the developed framework, the article discusses the implications of the comparison between the traditional and OC KPIs and the practical implementation of OC KPIs, supporting a paradigm shift towards a more occupant-centric assessment of building performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".