Comprehensive Simulation-based Workflow to Assess the Performance of Occupancy-based Controls and Operations in Office Buildings
Bibliographic record
Abstract
Although many people have been forced to work remotely due to the pandemic, occupancy patterns in office buildings were already rapidly changing due to technological advances, enabling workers to work remotely or "telework".While having more workers telework could potentially lower an organization's need for real estate and associated costs, this style of work is not implemented in the most efficient way since many buildings still consume significant amounts of energy even if occupancy is low.Additionally, spaces are acquired assuming maximum occupancy, and cleaning and maintenance schedules do not change with respect to occupancy level, therefore wasting resources.Most services provided -from ventilation to cleaning and space -are (or should be) a function of occupancy.Yet occupancy is seldom measured in a comprehensive way such that the data can be widely used to improve building operations.There is a wide variety of applications for such data and sensing technologies to collect it, but no existing frameworks for matching the two for optimal life cycle operations.The proposed research seeks to develop a workflow whereby long-term occupancy data is measured in office buildings, a range of advanced technologies and operating strategies are modelled, and then those are simulated to inform operators of the optimal approach for building operation and maintenance.A selection tool is developed to aid building practitioners to choose the most appropriate occupancy sensing technologies for a given set of applications.The selection process begins by first identifying the data requirements and characteristics for the selection of applications and defining the characteristics of occupancy sensing technologies, and then analyzing their alignment for optimal occupancy sensing technology selection.Later, a methodology is introduced to quantify the cost, energy, and GHG emission savings for PrefaceThe integrated thesis herein consists of two journal papers, either published or in process to be completed.However, readers who wish to refer to materials from this document should cite this thesis.The following articles are contained within this thesis
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".