Modelling the impact of occupant behaviour on direct load control of HVAC systems
Bibliographic record
Abstract
Direct load control (DLC) algorithms for HVAC systems are automated temporary interventions to the sequences of operation to reduce on-peak electricity demand. While DLC of HVAC systems has the potential to dramatically reduce the economic, societal, and environmental burden of electrification, occupant behaviour, specifically thermostat use, accounts for major uncertainty on this potential [1]. This study first develops a thermostat use behaviour model upon longitudinal field data of office occupants. The model represents both the stochasticity of an individual’s thermostat use patterns and the inter-occupant diversity. The model is then incorporated to EnergyPlus through its Python API. Seven DLC algorithms are examined at varying setback/setup intensities. Of them, three were without preconditioning and four were with preconditioning. Simulations were conducted with the EnergyPlus model of a small commercial building in Toronto, Canada. The results indicate that occupant behaviour can reduce the median on-peak demand savings by up to 20%, particularly with DLC algorithms with more than 2°C setback/setup and without preconditioning. Preconditioning could significantly reduce the risk of occupant overrides and improve the robustness of DLC to occupant behaviour.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".