Development of Inverse Greybox Model-Based Virtual Meters for Air Handling Units
Bibliographic record
Abstract
Energy submetering at the equipment level provides a tool to identify energy use anomalies and detect operational inefficiencies. While physical meters can be costly and difficult to install, virtual meters (VMs) overcome practical issues associated with physical meters and provide insights into critical unmeasured quantities. This article introduces an inverse greybox model-based virtual metering method to estimate the energy in an air handling unit (AHU). Models that represent the components typically found in AHUs are formulated using a data set from a highly instrumented AHU and combined into an integrated greybox model. The use of the integrated model to create VMs is demonstrated by using a data set from an independent AHU located in a large office building in Ottawa, ON, Canada. Model parameters are estimated by using the genetic algorithm and used in creating VMs that can estimate the heat supplied/extracted at the AHU level. In addition, the model is used to estimate a monthly average outdoor air fraction used by the AHU. The potential of the component models and VMs to detect operational inefficiencies and support operational decisions is demonstrated through illustrative examples. Note to Practitioners-This article presents a novel virtual metering algorithm to estimate the heating and cooling energy at the air handling unit (AHU) level. This virtual metering algorithm fills a gap in the literature and provides a tool that will help detect and interpret energy use anomalies, identify operational inefficiencies, and guide on-going commissioning of building energy systems. Facility managers, operators, and other stakeholders can use the insights gained from virtual metering to improve building operational performance. Future planned research includes developing virtual meters to characterize energy flows at the zone level and visualization methods to make inverse modeling results more accessible to different building stakeholders.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".