Investigation of a model predictive controller for use in a highly glazed house with hydronic floor heating and cooling
Bibliographic record
Abstract
Presented are the results of an investigation into a model predictive controller (MPC) for hydronic floor heating and cooling in a highly glazed house in Ottawa, Canada. The goal of this investigation was to determine if a MPC would simultaneously result in reduced energy consumption and reduced indoor air temperature (Tia) violations in comparison to the incumbent reactive controller (RC). Shown both experimentally and via simulation, predictive control results in more hours with Tia between the acceptable limits (19.5–25 °C) for significantly less heating and cooling system operation hours. Experimentally, a “pseudo-predictive controller” (PPC) required 35% less cooling hours and 50% less heating hours than the RC baseline. The PPC also reduced overheating time by 88%. A simple MPC was then designed and compared to a RC using a MATLAB simulation. Even with a simple and non-optimized MPC, the simulation confirmed the superiority of said MPC over RC operation for the hydronic floor system. The simulated MPC required 28% less cooling hours and 27% less heating hours than the simulated RC. These results have confirmed the suspected promise of predictive control and next steps include actual implementation of the presented MPC in the highly glazed house long-term.
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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) | 0.000 | 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) | 0.002 | 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".