A Dynamic Water-Filling Method for Real-Time HVAC Load Control Based on Model Predictive Control
Bibliographic record
Abstract
Heating ventilation and air-conditioning (HVAC) system can be viewed as elastic load to provide demand response. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, while still guaranteeing user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Second, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".