Bottom-up framework for modelling occupancy-based demand-side management strategies in a mixed-use district
Bibliographic record
Abstract
In the context of electrification for different sectors, demand-side management (DSM) strategies are acknowledged as primary strategies to ensure the stability and reliability of the utility grid. Urban building energy modelling (UBEM) emerges as a critical tool for utilities to assess the impact of these strategies on the building sector's energy consumption and flexibility. However, relying on the developed models for this task is applicable only when detailed occupant-related inputs are integrated into the model. To this end, this paper aims to develop a framework to integrate high-resolution occupancy schedules into UBEM and showcase the application of the developed models in evaluating DSM strategies with different scenarios. The developed framework is applied to a mixed-use district in Montreal, Canada with 112 buildings as a case study. The main objectives of this study are 1) developing an urban scale high-resolution occupancy profile generator representative of Canadian commercial buildings using mobile positioning data, 2) investigating the diversity between the generated profiles of buildings within the same type, 3) developing a method to integrate the generated profiles into the Canadian commercial archetypes, and 4) evaluating the applicability of the developed model in evaluating DSM strategies by investigating the effect of occupant-centric control and occupancy-based demand response strategies on the modelled district energy use. The results of this study serve as a preliminary investigation into the crucial role that occupancy patterns can play in maximizing building energy flexibility with an estimated reduction in district peak demand by up to 17%. This study also paves the way for future research incorporating occupant feedback and comfort requirements for a more precise exploration of the proposed strategy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".