A Metadata Inference Method for Building Automation Systems With Limited Semantic Information
Bibliographic record
Abstract
Metadata in most existing building automation systems (BASs) is inconsistent, incomplete, and nondescriptive. This situation is a major obstacle to the widespread use of data analytics to improve the operation of buildings. In this article, we put forward a method to infer zone-level metadata from features derived from BAS data. The method includes two steps: 1) classification of BAS points into different types (e.g., indoor temperature, indoor temperature set point, airflow, airflow set point, damper position, and radiator valve position) and 2) association of BAS points based on their functional relationships (i.e., grouping the sensors, actuators, and set points of each zone together). The metadata inference method was demonstrated with data from zones served by four different air handling units (AHUs) in two office buildings in Ottawa, ON, Canada. The results from this case study indicate that common zone-level BAS point types can be accurately classified and associated even in the absence of intuitive data labels. Note to Practitioners-This article was motivated by the problem of metadata normalization in existing buildings, in order to scale up the application of smart building solutions in the real world. Existing metadata normalization approaches mainly focused on inferring the point types of the metadata with both semantic (label) and numerical information (time series readings). In this article, we put forward a method to infer zone-level metadata with numerical information only. Methods for both types of classification and relationships' association of the BAS points are investigated. The results from two office buildings indicate that the classification phase can achieve an average of 90% accuracy, while the association phase can obtain an average of 85% accuracy. The method was developed and demonstrated with a limited data set by using data exclusively from zone-level sensors, actuators, and set points. Future work is planned to extend the proposed method to more comprehensive BAS data sets with the system- and plant-level data as well.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".