Rapid assessment of building operational strategies using control-oriented archetypes: a case-study with dual-energy heating and thermal storage
Bibliographic record
Abstract
Abstract This paper investigates the application of control-oriented archetypes as a pathway to develop advanced control strategies of general applicability. These archetypes consist of low-order resistance-capacitance thermal networks, which balance simplicity and accuracy, thus making them practical for rapidly comparing operational design and control options. To illustrate this methodology, several design and operational scenarios were investigated in an archetype model of an existing school building that uses an electric boiler as its primary heating system and a gas boiler as a backup. As a potential upgrade, an electricity-heated thermal energy storage device was also considered. The model was validated using energy bills. Two design parameters (electric boiler size, thermal storage capacity) and three operational variables (temperature setpoint, boiler operation, thermal storage charging/discharging) were investigated. Numerical results demonstrate that advanced control can significantly affect building performance, which consists in a trade-off between flexibility, costs and greenhouse gas emissions. These results exemplify the potential of control-oriented archetypes for supporting the development of operational strategies; such a generic method could be applied to other building and HVAC system configurations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".