Evaluating common supply air temperature setpoint reset strategies with varying occupancy patterns and behaviours
Bibliographic record
Abstract
The supply air temperature (SAT) setpoint of a multi-zone variable air volume (VAV) air handling unit (AHU) systems significantly affects the system’s performance. ASHRAE Guideline 36 introduces a so-called trim and respond logic defining the SAT reset behaviour of these systems. The trim and respond logic for SAT reset relies on demand-based feedback. Many studies have assessed ASHRAE Guideline 36, however there is a literature gap in addressing the performance of the trim and respond SAT reset with varying occupancy patterns and behaviours. This paper studies four SAT reset strategies under different thermal preferences and irregular occupancy patterns: (1) constant 13°C SAT, (2) SAT reset based on outdoor air temperature (OAT), (3) trim and respond, and (4) trim and respond combined with OAT reset. Different cases of zone-level setpoints and irregular occupancy schedules have been simulated in EnergyPlus with the studied SAT setpoint reset methods. The results show that varying setpoints across different zones lead to higher energy use with all studied SAT reset strategies. The highest variation in energy use was accompanied with constant SAT, with a standard deviation of 16 kWh/m 2 , and the highest variation in averaged discomfort fraction was accompanied with OAT reset, with a standard deviation of 5.3%. Both trim and respond methods achieved better comfort results with varying setpoints. These findings establish a basis for future work on developing a SAT reset strategy that utilizes occupant-centric control (OCC) that optimally balances thermal comfort and energy use. • Studying supply air temperature reset strategies with varying occupancy behaviour. • Pure trim and respond consumed least energy with varying zone setpoints. • Varying zone setpoints did not affect comfort with trim and respond SAT reset. • Simultaneous cooling and heating occurred due to conflicting zone demands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".