Modelling the impact of occupant behaviour on direct load control of HVAC systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it