Building IoT Systems Modeling: A Object-oriented Metamodeling Approach
Bibliographic record
Abstract
Buildings are supersystems consisting of many constituent systems that require coordination and interaction to function coherently. Modeling buildings and representing them in both readable forms by humans and computers is important for efficient coordination, simulation, control, and optimization of the systems within buildings and for the integration and creation of other novel applications and functions.Most commercial buildings come with a Building Energy Management System (BEMS), which has representations of aspects of a building and its systems. These representations in the BEMSs, however, do not wholly model a building (and its spatial entities), its systems and subsystems, and their relationships. This is because their primary function is to control heating, ventilation, air-conditioning (HVAC), and lighting.Although there are many studies on modeling the built environment for operational purposes, recently, Project Haystack and Brick have made good progress by adopting a metadata approach to modeling and providing readable representations of buildings and their systems. However, the tags and tagset systems have some limitations that stem from the lack of structure in defining entities, their properties, and relationships.In this study, we identify five problems with the metadata approach to modeling the built environment and attempt to address these problems with an object-oriented metamodel: Energy Things Metamodel (ETM), that fully models a building, its systems, subsystems, and their relationships. ETM maintains a consistent naming convention of entities with other projects like (Project Haystack and Brick) and uses an object-oriented approach to modeling, producing a metamodel that best represents a building and its systems. In this study, a UML implementation of the core structure of ETM is achieved.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".