Stochastic-based occupant-centric building archetype modelling using plug loads
Bibliographic record
Abstract
To achieve today’s immediate climate goals, Quebec has provided the Green Economy action plan targets to achieve a 37.5% greenhouse gas emissions reduction compared with 1990 levels and reach carbon neutrality by 2050. During this time, the built environment has a high potential for energy demand reduction. Urban building energy modeling (UBEM) can support building energy management in the urban context. The occupant-related schedules (e.g., presence and interaction with energy systems) significantly impact the UBEM’s uncertainty. In most existing urban-scale building energy models, fixed default occupant-related schedules are typically used, which might not necessarily capture the variation associated with occupancy. The main reason is the lack of data available to model dynamic occupancy schedules leads to differences between energy simulation results and the actual data. Without a more complex occupancy model within UBEM, it is impossible to achieve a reliable energy demand estimation and peak load prediction. Therefore, for a more robust output from building energy simulation, UBEM requires occupant-related schedules that include the variability and diversity of the occupant behavior.Knowing the critical roles of occupants in a building’s energy use and management, stochastic occupant-centric archetypes are a promising way to support the variability and stochasticity of the occupant-related schedules to simulate district demand more accurately. A more realistic district load curve can be obtained if stochastic occupant-related profiles are correctly modeled. Previous research on stochastic occupant-related schedules can only be used in building energy simulation of specific buildings, such as office and residential, not for all buildings in mixed-use districts. Thus, this article outlines a framework to extract the representative occupant-related profiles from time-series data for mixed-use neighborhoods and model their performance considering the stochastic nature of occupant behavior. Also, it could be demonstrated how the stochastic-based occupant-related archetypes improve the urban building energy modeling workflow to predict demand. This dynamic model could provide relatively accurate simulation results and pave the way to identify appropriate energy management strategies.The output illustrates that demand modeling for neighborhoods with identical building types gives unrealistically high heating, cooling, and electricity peaks where fixed occupancy schedules are assigned to the model. Besides, applying stochastic-based schedules can include the variability of the occupant behavior in the model where similar archetypes are to the neighborhood buildings. Overall, the proposed framework integrates flexible and reliable occupant-centric archetypes and energy demand analysis, including forecasting the impacts of the variability of occupant behavior to establish an informed basis for energy-efficient strategies and demand-side energy 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".