A comprehensive variable refrigerant flow heat recovery model for building performance simulation
Bibliographic record
Abstract
A comprehensive, physics-based, and modular Variable Refrigerant Flow with Heat Recovery (VRF-HR) model is developed for multi-year simulations of large-scale VRF systems. The model is designed to simulate various operational modes, including single-mode (cooling-only, heating-only) and heat recovery mode, across any number of indoor units (IUs), outdoor units (OUs), and compressors. A parameter-estimation procedure leveraging manufacturer data is implemented to calibrate the model, ensuring accurate system representation. A machine learning-based control strategy is introduced to emulate real-world compressor selection for partial load operation. The model is validated using two years of operational data from a large-scale VRF system serving the first floor of the former ASHRAE Headquarters Building in Atlanta, USA, which consists of 22 indoor units, 2 outdoor units, and 8 compressors. Results demonstrate that the manufacturer-tuned model accurately predicts total energy consumption, achieving a relative error of 9.5%, an NMBE of 6.2%, and a CVRMSE of 27.2% over the first year. For the second year, the model achieves a CVRMSE of 25.3%, an NMBE of 5%, and a relative error of 7%, meeting ASHRAE calibration criteria.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".