Coordination of radiant floor and baseboard heating systems: Sequential and simultaneous MPC schemes
Bibliographic record
Abstract
Two novel model predictive control (MPC) schemes are proposed in this article for coordinating two different heating systems with fast and slow heating dynamics. The objective is to improve the performance of a slow-reacting heating system in terms of maintaining the indoor operative temperature within predefined bounds while reducing the energy cost. Here, a combination of a hydronic radiant floor heating (RFH) system and electric baseboard (BB) heaters is used for the demonstration. A sequential approach is proposed where separate MPC optimizations are performed sequentially for the RFH and BB heaters, whereas for the simultaneous approach a single MPC optimizes the two heating systems concurrently. The performances of these two cooperative schemes are compared with the base case where the RFH is used as the only heating system. The simultaneous approach results in achieving improved comfort with a 6% reduction in the energy cost compared to the base case. The RFH system, for both the base case and the cooperative setups, uses a configuration incorporating a heat pump and a thermal energy storage (TES) tank for optimal energy usage based on the time-of-use electricity rates.
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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 | 0.000 | 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) | 0.001 | 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".