Development and implementation of a data-driven model predictive controller for hydronic floors: an experimental case study
Bibliographic record
Abstract
Despite the growing popularity of model predictive controllers (MPCs) in building automation, there are few investigations of MPC in residential buildings. However, existing studies show promise for MPCs in buildings with high glazing, and radiant conditioning. This paper presents the development and results of a long-term MPC implementation at a full-scale research house during the heating season. The MPC manages the operation of hydronic floors to maintain thermal comfort while minimizing energy use. This work includes a novel MPC approach, forecasting approach, and parameter estimation technique. An existing data-driven model and sequential parameter estimation approach were modified and used in this work. Disturbance forecasting (including solar gains, equipment, and heat transfer to the ground, and water storage tanks) employs a clustering algorithm, then rule extraction with a decision tree. Compared to a rule-based controller (RBC), the MPC reduced energy use by 10%, and decreased the magnitude and duration of overheating.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".