An optimal predictive control strategy for radiant floor district heating systems: Simulation and experimental study
Bibliographic record
Abstract
A study was conducted to assess the energy performance of an optimal predictive control strategy for radiant floor district heating systems. A four-zone radiant floor heating system model was developed. The simulated performance of the optimal predictive control strategy was studied. The results showed 10% energy savings compared to a Proportional-Integral (PI) control strategy. Experiments were conducted in a laboratory radiant floor district heating system test facility. The description of the test facility, its operating conditions, and the results obtained are described. Experimental results further confirm the findings from the simulation study. Being simple and energy efficient, the optimal predictive control strategy is a good candidate control strategy for radiant floor district heating systems. Practical application: Energy efficiency is a major issue of interest in the design and operation of sustainable heating systems. The radiant floor district heating systems have been successfully installed and operated in many countries resulting in significant energy savings. The optimal predictive control strategy proposed in this study further enhances the potential for higher energy savings from the district heating systems. The optimal control strategy is simple to implement as it relies on the predicted outdoor air temperature and computes future temperature set-points for the boiler water temperature. A suitably tuned Proportional-Integral (PI) controller can be used to track the optimal set-point thus realizing potential energy savings. A programmable logic controller or a supervisory control system would be appropriate to implement the designed optimal control strategy. The local control can be realized by using industrial PI feedback controllers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".