Diagnostics for Monitoring-Based Commissioning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Synopsis This paper presents a case for application of automated monitoring, analysis and diagnostic tools for monitoring-based commissioning. Selected examples are presented in which such tools have been used successfully to support commissioning activities in southwestern Canada and the U.S. Pacific Northwest. The first example involves use of spreadsheet-based tools to automatically generate diagnostic plots that are visually examined for specific features that reveal operational problems in space conditioning systems of large commercial buildings. The findings then guide re-tuning actions to increase building energy efficiency. This is followed by application of a tool for continuous monitoring of whole-building energy use to automatically track energy savings resulting from a utility commissioning program. This tool also provides a means by which to detect degradation of savings and performance to guide monitoring-based commissioning actions. The potential use of automated diagnostic tools for chillers and packaged air conditioners is then described for continually commissioning these units. The paper concludes with a discussion of the impacts of this approach on commissioning, including potential time savings, associated cost savings, and improvements in the quality of commissioning.
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it