Development of an Occupant-centric Control Algorithm for Mixed-Mode Ventilation Buildings to Regulate Window Operations
Bibliographic record
Abstract
Mixed-mode ventilation is a design feature to improve building energy efficiency and indoor air quality by combining natural ventilation and mechanical ventilation.Mixed-mode ventilation commercial buildings are often equipped with variable air volume (VAV) terminal device and air handling unit (AHU) as the mechanical ventilation system and operable windows to deliver natural ventilation.However, in practice, mixed-mode ventilation buildings do not always achieve better performance than mechanically ventilated buildings, largely due to inappropriate window operations.Therefore, the sequences of operation for terminal devices serving zones with operable windows should be designed in recognition of these risks, which in turn should be informed by research investigating occupants' window and thermostat use behaviour.This research examines window and thermostat use data collected from two mixed-mode ventilation buildings in Ottawa, Canada.Discrete-time Markov logistic regression models and decision tree models were established to predict the likelihood of thermostat keypress and window opening/closing instances and identify the indoor conditions that trigger these actions.Based on this analysis, a set of control algorithms are developed to improve terminal device sequencing in mixed-mode ventilation buildings in cold climates such that the comfort and energy savings potential of operable windows can be fully realized.The control algorithm applies a thermostat setpoint setback to encourage occupants to open windows when conditions are advantageous for saving energy, and discourage occupants from opening windows when energy penalties may be caused.The control algorithms are tested by using building performance simulation (BPS), and 3-16% of energy reductions could be achieved when control sequences encouraged occupants to undertake energy-efficient window use behaviours compared to an identical buildings with unregulated window operations.It is also found that the unregulated window operations could increase the heating load up to 21% and cooling load by 22% relative to identical buildings with fixed windows in a cold climate.These findings suggest that control algorithms should be designed properly in mixed-mode ventilation buildings to realize its full energy-saving potential and avoid adverse energy impacts caused by unregulated window operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".