Grey-box models of chiller evaporator for practical integration in building automation systems
Bibliographic record
Abstract
This paper presents the development and validation of grey-box models for estimating the chilled water temperature difference ΔTchw across the chiller evaporator, with potential applications as virtual sensors in building automation systems (BAS) or integration into other mathematical models. The models are established for two scenarios, variable and constant chilled water flow rates under quasi-steady-state operation. These models require a small number of input variables and are characterized by strong adaptability. Three case studies of different chillers are used to validate the proposed virtual sensors with both static and dynamic windows methods, and help in the generalization of the proposed method. The models demonstrate high accuracy and robustness, achieving a root-mean-squared error of 0.19 °C in one case study. This study addresses the gap in the availability of simple yet reliable models that can be practically integrated into building automation systems for virtual sensing, virtual calibration, fault detection and diagnosis, and HVAC system control and optimization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".