Energy Flexibility Comparison of Different Control Strategies for Zones with Radiant Floor Systems
Bibliographic record
Abstract
Radiant floor systems offer significant potential for studying and developing energy flexibility strategies for buildings and their interaction with smart grids. Efficient design and operation of such systems require several critical decisions on design and control variables to maintain comfortable thermal conditions in the space and floor surface temperatures within the recommended range. This study presents a comparison of different control strategies to activate energy flexibility for zones with radiant floor heating systems. The focus of this study is on the zones with radiant floor systems for which the hydronic pipes are located deep in the concrete and therefore, there is a significant thermal lag. A perimeter zone test room equipped with a hydronic radiant floor system in an environmental chamber is used as to validate the modelling methodology. Considering a typical cloudy and cold winter day, three different control strategies for radiant heating were studied based on controlling the zone air temperature, floor surface temperature, and the operative temperature. Then considering morning and evening peak demand periods, the downward and upward energy flexibility are quantified and compared with each other for the different control strategies. It is observed that for the same 2 °C increase or decrease in the setpoint, the control strategy based on the zone air temperature results in the higher flexibility for both downward and upward scenarios compared with the floor surface and operative temperature controls. The effect of increasing window to wall ratio (WWR) is also investigated. Then, also the effect of solar gains on a sunny day on energy flexibility is studied. No significant difference in the upward and downward flexibility is observed. However, the hours of zero heating load are significantly increased due to the contribution from the solar gains.
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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 | 0.000 | 0.000 |
| 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".