Online model-based predictive control with smart thermostats: application to an experimental house in Québec
Bibliographic record
Abstract
This paper tests the impact of model resolution and structure on the performance of Model Predictive Control (MPC) implementation in an unoccupied research house in Québec equipped with smart thermostats. Two low-order models and a high-order multi-zone model were calibrated with measured data, with the structure of the multi-zone model being generated automatically during the calibration procedure. The three models were used to apply real-time MPC to an experimental house in Québec using the established dynamic tariffs for morning and evening peaks. MPC with any of the three models successfully preheated the house before the demand-response events, outperforming the reference reactive controller, reducing cost and thermal discomfort. The high-order multi-zone model performed the best, reducing average cost of electricity by 55% and high-price energy consumption by 71%, compared to the low-order models, which achieved cost reductions of 40% and 44% and energy consumption reductions of 48% and 54% respectively.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".