Model-Based Control Strategies to Enhance Energy Flexibility in Electrically Heated School Buildings
Bibliographic record
Abstract
This paper presents a general methodology to model and activate the energy flexibility of electrically heated school buildings. The proposed methodology is based on the use of archetypes of resistance–capacitance thermal networks for representative thermal zones calibrated with measured data. Using these models, predictive control strategies are investigated with the aim of reducing peak demand in response to grid requirements and incentives. A key aim is to evaluate the potential of shifting electricity use in different archetype zones from on-peak hours to off-peak grid periods. Key performance indicators are applied to quantify the energy flexibility at the zone level and the school building level. The proposed methodology has been implemented in an electrically heated school building located in Québec, Canada. This school has several features (geothermal heat pumps, hydronic radiant floors, and energy storage) that make it ideal for the purpose of this study. The study shows that with proper control strategies through a rule-based approach with near-optimal setpoint profiles, the building’s average power demand can be reduced by 40% to 65% during on-peak hours compared to a typical profile.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".