Comfort-aware Optimal Space Planning in Shared Workspaces
Bibliographic record
Abstract
Energy consumption in office buildings, especially in shared office spaces, can be substantially reduced through joint optimization of space use and heating and cooling demands. This paper addresses this underexplored research problem in a coworking space that offers long-term and daily plans. We train an input convex neural network to estimate the energy consumed by the HVAC system in a single day to condition a given zone of the building. Due to the convexity of this model in its inputs, we formulate a convex mixed-integer program to optimize HVAC energy consumption by deciding how to assign desks to occupants and adjust zone temperature setpoints. Considering a medium-sized office building as the coworking space, we show that this optimization problem can be solved to near-optimality relatively quickly, hence it can be used to make decisions regarding long-term bookings. For daily bookings, we design heuristic algorithms that take the solution of the optimization problem and assign the remaining space, while ensuring the satisfaction of thermal comfort constraints. By incorporating these algorithms in the workspace reservation system, energy consumption can be reduced by up to 11.7% while maintaining individual thermal comfort.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".