Predictive control of radiant floor heating and solar-source heat pump operation in a solar house
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
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.
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 | 0.001 | 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 | 0.000 |
| 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