AN INTERACTIVE WORKBENCH FOR MONITORING , IDENTIFICATION AND CALIBRATION OF BUILDING ENERGY MODELS
Bibliographic record
Abstract
This paper presents a concept and main capabilities of the Matlab-based Building Energy Modeling (BEM) Workbench developed at Ryerson University (Toronto, Canada). The workbench was designed as an interactive tool intended to facilitate and to provide common media for data processing tasks related to various building energy modeling procedures such as (i) on-site data monitoring, (ii) preparation of input data and analysis of simulation results, (iii) validation, verification and calibration of building energy models and (iv) estimation of building thermal parameters. To illustrate the use of the BEM-Workbench several working scenarios are presented. Known inputs from literature methodologies of building thermal parameter estimation were implemented into the workbench to demonstrate one of its purposes as a hypothesis testing tool. Another scenario was introduced to show how the workbench can be used to analyze a buildings model’s dynamic behavior, a critical step in the model’s calibration procedure. Programmatically, the workbench is configured as a set of Matlab GUI components and functions with capability to further expand its functionality.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".