Toward machine learning-based prognostics for heating ventilation and air-conditioning systems
Bibliographic record
Abstract
Fault detection, diagnostics, and prognostics (FDD&P) can greatly help improve the performance of building operations by reducing energy consumption for heating, ventilation and air-conditioning (HVAC) while maintaining occupant comfort at the same time. In particular, prognostics as an emerging technique, is attracting an amount of attention from building operators and researchers because it enables a pro-active fault prevention strategy through continuously monitoring the health of building energy systems. In this paper, we propose to develop a machine learning-based method for HVAC prognostics. Building on techniques from machine learning and data mining, the proposed methods can help develop predictive models from the historic building operation and maintenance data. After presenting the proposed method, we discuss the building operation simulation conducted to generate data for evaluating the feasibility and usefulness of the proposed methods. The results from these numerical experiments demonstrated that the machine learning-based methods can be effective for HVAC prognostics.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".