Inverse Greybox Virtual Metering Algorithms to Visualize Building Energy Flows
Bibliographic record
Abstract
Energy submetering at the equipment level provides a tool to control energy consumption and improve equipment energy use.Virtual meters (VMs) are a cost-effective alternative to physical meters that can capture unmetered energy flows at the equipment level and provide building stakeholders with details that support their operational decisions.The virtual metered energy can be presented through interactive visualizations that allow users to interact with the graphical representations based on their operational needs to gain insights that would facilitate decision-making processes.This thesis aims to develop a suite of virtual metering algorithms to characterize unmeasured energy flows across critical heating, ventilation, and air conditioning components and present the virtual metered energy through effective visualizations that allow user interaction to gain insights into building operational decisions.To this end, the thesis structure consists of three main parts that develop equipment-level virtual meters and a standalone visualization tool to visualize the virtual metered energy.In this first part, an integrated inverse greybox AHU model is developed using data collected from a highly instrumented AHU serving an academic building in Ottawa, Canada.Optimal values of model parameters are used to create VMs that estimate the heat supplied by the heating coil, the heat extracted by the cooling coil, and the heat gains due to the supply fan.In the second part, VMs that estimate the heat added by zone-level perimeter radiant heaters are developed using steady-state, transient, and load disaggregation inverse modelling approaches.The models are trained using data collected from 18 zones in an academic building in Ottawa, Canada.The accuracy of the VMs is assessed by comparing the heat estimated by the VMs to measurements obtained from physical meters installed in the 18 zones.The modelling approaches' performance is driven Building Operation and Maintenance (DBOM) Lab, and Building Performance Research Center (BPRC) at Carleton University.Their positive attitude and sense of humour helped me turn challenges into achievements.I want to express my appreciation
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".