Performance of VRF systems based on large scale monitoring
Bibliographic record
Abstract
Abstract Variable Refrigerant Flow (VRF) systems are refrigerant systems, which are generally comprised of an outdoor unit serving multiple indoor units connected by a refrigerant piping network. It is important to evaluate the performance of VRF systems, which can help the design and operate of VRF systems. Performance test done by manufactory can reveal the performance of VRF systems in designed conditions. However, it is hard to reveal effective performance in real buildings. The field test is complicated compared with the test in the laboratory and can only conduct on typical samples rather than large scale samples. However, typical samples are not enough for reflecting the performance of large scale VRF systems samples. A simple method of evaluating the performance of large scale VRF systems samples is necessary. This paper proposed and calculating model for electricity consumption and cooling demand of VRF systems based on measured operating data in the laboratory. The paper used the calculating model combined with 344 samples operating data from real residential buildings to calculate the performance of a large scale VRF. This paper analyzed the VRF systems’ performance with different influencing factors such as climate zones, cooling duration and outdoor temperature for the recommendation for VRF systems’ designing and operation.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".