Using Personal Environmental Comfort Systems to Mitigate the Impact of\n Occupancy Prediction Errors on HVAC Performance
Bibliographic record
Abstract
Heating, Ventilation and Air Conditioning (HVAC) consumes a significant\nfraction of energy in commercial buildings. Hence, the use of optimization\ntechniques to reduce HVAC energy consumption has been widely studied. Model\npredictive control (MPC) is one state of the art optimization technique for\nHVAC control which converts the control problem to a sequence of optimization\nproblems, each over a finite time horizon. In a typical MPC, future system\nstate is estimated from a model using predictions of model inputs, such as\nbuilding occupancy and outside air temperature. Consequently, as prediction\naccuracy deteriorates, MPC performance--in terms of occupant comfort and\nbuilding energy use--degrades. In this work, we use a custom-built building\nthermal simulator to systematically investigate the impact of occupancy\nprediction errors on occupant comfort and energy consumption. Our analysis\nshows that in our test building, as occupancy prediction error increases from\n5\\% to 20\\% the performance of an MPC-based HVAC controller becomes worse than\nthat of even a simple static schedule. However, when combined with a personal\nenvironmental control (PEC) system, HVAC controllers are considerably more\nrobust to prediction errors. Thus, we quantify the effectiveness of PECs in\nmitigating the impact of forecast errors on MPC control for HVAC systems.\n
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".