Control-oriented thermal network models for predictive load management in Canadian houses with on-Site solar electricity generation: application to a research house
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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".