Development and implementation of a data-driven model predictive controller for hydronic floors: an experimental case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it