Field implementation of model-based predictive control in an all-electric school building: Impact of occupancy on energy flexibility
Bibliographic record
Abstract
Integrating advanced control strategies is essential in reducing energy cost, optimizing interaction with the electric grid, and enhancing indoor environmental quality in buildings. Enhancing energy flexibility in the building demand profile is essential for the safe and efficient operation of smart grids. This paper presents a grid-interactive model predictive control methodology to improve energy flexibility and maintain indoor environmental quality in school buildings. The proposed model predictive control framework employs data-driven grey-box thermal network models for classrooms with convective heating systems. The methodology is implemented in an all-electric school building in Montreal, Canada, during very cold winter days. Two control scenarios are investigated and compared: 1) a reference scenario using a proportional-integral controller and business as usual thermostat setpoints and 2) a flexible control scenario using model predictive control. Both scenarios were tested under two conditions: with and without occupants in classrooms. Four classrooms operated with proportional-integral controller and usual setpoint profiles were considered reference cases, while another four classrooms employed model predictive control as flexible cases. Results showed that the model predictive control increased energy flexibility by 36% in unoccupied conditions and 61% in occupied conditions while reducing energy consumption by 25% and 63%, respectively. Throughout both scenarios, the model predictive control maintained satisfactory thermal comfort and indoor air quality. This approach is scalable and transferable to other institutional or mid-size commercial buildings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".