Impact of operative temperature on the performance of control system: Field application in a grid-interactive building
Bibliographic record
Abstract
Abstract This paper presents the field application of a model predictive control framework for HVAC systems, using operative temperature as the control variable. The case study is an all-electric school building located in the Montreal area, Canada. A novel multi-sensor device was deployed to capture air and mean radiant temperatures, enabling real-time estimation of operative temperature and precise calibration of grey-box models. The control performance is evaluated by comparing two scenarios: 1) using air temperature as the control variable and 2) using operative temperature as the control variable. Moreover, two control scenarios are investigated, including proportional-integral control as a baseline and MPC as an advanced control strategy. Results demonstrate that using operative temperature improves the accuracy of comfort assessment and control performance. This approach achieves up to 57% peak demand reduction in response to utility tariffs while maintaining occupant comfort. This study demonstrates the importance of integrating operative temperature measurements in MPC applications for enhanced energy flexibility and thermal comfort in institutional and commercial buildings.
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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) | 0.000 | 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) | 0.001 | 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".