How Can Stochastic Occupant-Based Archetypes Enhance Urban Building Energy Modeling for Better Decision-Making?
Bibliographic record
Abstract
Urban building energy modeling (UBEM) is widely used to support energy planning at neighborhood and city scales. However, most existing building archetypes rely on deterministic occupant-related schedules that do not adequately capture real occupancy variability, which can reduce the accuracy of energy demand predictions. This study examines the effect of incorporating stochastic occupant-related schedules into UBEM using a stochastic occupant-centric archetype framework applied at the campus scale. The stochastic schedule generation methodology, previously developed by the authors, is applied to generate occupant-related schedules from measured datasets. Three scenarios are evaluated: (1) a standard deterministic approach, (2) a stochastic approach based on historical campus electricity data, and (3) a stochastic approach derived from the Building Genome Project dataset. A validation framework is implemented to assess the influence of occupant-related schedules on electricity demand predictions. The results demonstrate that incorporating stochastic schedules derived from measured data significantly improves UBEM predictive accuracy. Scenario 2 achieved the best performance, showing an average improvement of 32.26% compared to the standard deterministic approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".