Dynamic modeling, adaptive control and energy performance simulation of a hybrid hydronic space heating system
Bibliographic record
Abstract
A dynamic model of a hybrid hydronic heating system has been developed. The overall model consists of a boiler, a heat exchanger, a ground loop heat pump, a ground loop heat exchanger, baseboard heaters (convector–radiators), and radiant floor hydraulic piping system. Two control strategies for improving the overall system performance were explored: (1) a conventional proportional-integral (PI) control and (2) an adaptive gain control. The simulation results showed that the performance of the adaptive controller is better than the fixed gain PI controller in disturbance rejection and stability. Energy simulations under three different operating strategies were conducted: (1) a fixed set-point PI control, (2) an outdoor air temperature reset control, and (3) an optimal set-point adaptive PI control. It was shown that the outdoor temperature reset strategy can save 4.5 and 19.9% energy under cold day and mild day conditions compared to the fixed set-point PI control strategy. The optimal adaptive PI control strategy resulted in higher energy savings of 6.6 and 22% as compared to the PI control under cold and mild day conditions, respectively. Practical application: Energy efficiency and sustainability are a major issue of importance in the design and operation of space heating systems. The proposed hybrid system combines a ground-source heat pump and a hot water boiler for space heating of both residential and commercial building applications. The heating system consists of radiant floor piping for the residential zones and hot water baseboard heaters (convector–radiators) for the commercial zones. An energy optimal adaptive control strategy is designed to improve energy efficiency and temperature control performance of the hybrid system. The control strategy is simple and can be implemented on the existing building control systems.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".