Simulation of HVAC Local Control Based on Occupants Locations and Preferences
Bibliographic record
Abstract
The Heating, Ventilation and Air Conditioning (HVAC) system consumes the major part of energy in buildings. By analyzing the gap between the actual energy consumed and the required energy to satisfy heating/cooling loads, it is estimated that an average of 38% of the energy can be saved with the adoption of more efficient control strategies. Occupancydriven management has attracted great attention due to the potential energy savings. Therefore, the development and implementation of these strategies are of primary importance. On the other hand, future smart buildings will have the ability to detect and locate the occupants, and adjust the HVAC system to better satisfy their preferences, which is expected to result in considerable energy savings. This paper aims to simulate the energy performance that can be achieved by applying local control of the HVAC system. It proposes an HVAC localized control strategy based on the locations and preferences of occupants in an office open space with multiple zones. In order to evaluate the energy saving after applying the control strategy, DesignBuilder software is used to model one office at Concordia University and simulate its energy consumption.
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.
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) | 0.000 | 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) | 0.004 | 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".