Control-oriented thermal network models for predictive load management in Canadian houses with on-Site solar electricity generation: application to a research 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
This paper presents a methodology for development of control-oriented thermal RC network models for optimized HVAC load management in typical electrically heated single-family two-storey detached houses in Québec, assuming that there are three zones and it is equipped with photovoltaics and battery storage. Using data from an unoccupied research house, two 3rd order RC networks are developed and calibrated. One model (3C6R network) assumes that each floor is a separate thermal zone, and the other model (3C7R network) assumes that the south-facing zone and the north-facing zone of the building are separate zones. Application of model predictive control with both developed models results in an average 12.1% reduction in the daily heating load, 19.8% reduction in the total daily electricity imported, 68.1% reduction in the peak demand, 67.0% reduction in the energy cost, and 13.4% increase in the self-consumption of on-site generated solar electricity compared to a traditional reactive controller.
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 | 0.001 |
| 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