Towards dynamic pricing algorithm for residential buildings: A Model Predictive Control framework for load aggregation
Bibliographic record
Abstract
With the rise of aggregators, establishing an effective pricing framework for electricity allocation among substations serving residential customers is crucial. This paper proposes a method for large-scale demand-side flexibility, smart thermostat data, along with ambient temperature and heating power measurements, are utilized to calibrate models of reduced-order resistance-capacitance (RC) thermal networks for buildings. A Monte Carlo framework evaluates the optimal level of demand-side management (DSM), strategy diversification, and uncertainty in aggregated demand, guiding an economic Model Predictive Control framework for day-ahead coordination market. Simulations conducted on numerous homes in Toronto, Ontario, demonstrate that implementing medium-to-high DSM can enhance grid stability, leading to a 94% increase in load factor. The findings suggest that a balanced portfolio of aggregation provides the most benefit, and a three-tier tariff structure results more effective for load flattening than a two-tier system. This approach offers a viable pathway to improve electricity grid management in residential areas.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".