Modelling thermostat use behaviour in multi-zone residential buildings: a real-world data study and simulation framework for peak demand management
Bibliographic record
Abstract
Recent research has highlighted the critical role of occupant behaviour in demand management strategies such as Demand Response (DR). This study leverages real-world data from 30 houses in Quebec, Canada, to analyze user behaviour across various zones. Generalized linear mixed-effects models were developed to predict occupants’ thermostat use patterns to capture the inter-household diversity. A simulation framework was then developed by integrating these models into a building performance simulation, enabling the testing of various DR scenarios. Results showed that scenarios with pre-heating achieved higher peak demand reductions, primarily due to lower override rates and the benefits of pre-heating itself. The Extreme scenario with pre-heating achieved an 81% reduction in peak demand. In addition, user overrides reduced peak demand reductions by up to 7%. The analysis also revealed that override rates vary by zone, with higher rates in living areas compared to bedrooms, which emphasizes the need for zone-specific DR strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".