Shrinked-Space Search Method for LVCTs' Parameters Identification
Bibliographic record
Abstract
Smart thermostats have become a promising device to control electric baseboard heaters' energy consumption while considering their flexibility in the demand response (DR) context. This article applies a shrinked-space search method to identify the tuning parameters of line voltage communicating thermostats (LVCTs). The proposed approach based on Bayesian optimization (BO) algorithm takes account of a reference model to drastically shrink the search space while enforcing the identification of a single set of parameters compatible with all arising dynamics of the controller and helping to establish an interpretable model. Furthermore, a subsequent integral tracking strategy has been adopted to convexify the objective function (for identification purposes) while considering the logical constraints governing the thermostat dynamics. This helps to recover the updating logic of the integral part of the controller model. The experimental validation results of eight LVCTs operating in an inhabited house show the effectiveness of the proposed method since it leads to establishing digital twins for the studied controllers. In addition, a case study is presented to demonstrate the usefulness of the reconstructed model in a DR framework.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".