Potential Performance Enhancement of a Solar Combisystem with an Intelligent Controller
Bibliographic record
Abstract
Solar thermal systems in residential buildings are generally controlled by two-level controllers, which activate solar thermal or at times with low solar radiation auxiliary energy supply into a thermal storage. Simple controllers do not have any information on actual or expected solar radiation. This leads to interference of auxiliary- and solar heat supply, which reduces the share of solar thermal energy fed into the thermal storage. Increasing accuracy of weather forecast data suggests incorporation of this information in the control algorithm. This work analyzes the maximum potential performance enhancement when applying such an intelligent predictive control. Two solar thermal systems with one auxiliary source respectively are designed in TRNSYS – these systems represent the base case. Further, a number of simulations are conducted with minor variations for the plant parameters – this gives generic results for different system configurations. In addition, each system configuration is altered to mimic the behavior of a plant with intelligent predictive control. Comparison of results indicates an improvement potential up to 10% for annual solar fractions and up to 30% for monthly solar fractions. The performance bound with respect to the annual auxiliary energy savings is approximately 8%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".