Predictive control of radiant floor heating and solar-source heat pump operation in a solar house
Bibliographic record
Abstract
Solar radiation can supply a significant portion of the energy requirements of a house through the harmonized use of passive solar design and building-integrated active solar energy systems (e.g., building-integrated photovoltaic, photovoltaic/thermal systems, or solar thermal collectors). Given the variability of solar radiation, energy storage technologies, along with carefully planned control strategies, can offer significant benefits for the performance of these systems in terms of energy consumption, peak load reduction, and thermal comfort for the occupants. This article investigates the application of a predictive control methodology for a solar house. The case study is a room with a simple geometry with high insulation and air-tightness values and large windows (i.e., a typical room found in a passive solar house). Predictive control is applied at two different, but closely linked, levels: (a) local-loop control of a radiant floor heating system and (b) supervisory control of the temperature of a water tank—used for thermal energy storage—heated with a solar-source heat pump. The development of control strategies is facilitated by the use of simplified building models obtained from more detailed models appropriate for building simulation. This methodology provides insight into the relevance of different design and control parameters and makes it easier to apply software tools designed specifically for testing control algorithms.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".