Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism
Bibliographic record
Abstract
When applying occupant models to building performance simulation (BPS), it is common practice to use a discrete-time approach requiring fixed time steps. Consequently, a simulated occupant's decisions do not increase in frequency in response to rapid changes in environmental conditions. Furthermore, as illustrated in this study through the analysis of a discrete-time EnergyPlus simulation, changing the time step between simulation runs may have a dramatic effect on BPS predictions. It is therefore necessary to adhere to a prescribed time step, which may complicate the synchronization of events when models of different domains are coupled. The main contribution of this study is an investigation of the viability of employing the discrete event system specification (DEVS) formalism to represent occupant behaviour without fixed and prescribed time steps. Results indicate that using an adaptive time advancement scheme, the DEVS formalism permits realistic patterns of decision-making while facilitating the coupling of stochastic occupant models with thermal and heating, ventilation and air-conditioning models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".