MetamEnTh: An Object-Oriented Metamodel for IoT Systems in Buildings
Bibliographic record
Abstract
Buildings consist of systems that have to interact and coordinate with various systems to function smoothly. It is essential to have models and representations of buildings in forms that are easy to read for effective simulation, control, and optimization of building systems. It is also necessary for integrating and creating novel applications and functions. A Building Energy Management System (BEMS) is a common feature of most commercial buildings. It contains models of some aspects of the building and its systems. However, these models in the BEMSs do not entirely model all systems and subsystems and their relationships because their primary function is to control heating, ventilation, air-conditioning (HVAC), and lighting. Project Haystack and Brick have made significant progress in modelling buildings for operational purposes by adopting a metadata approach. They offer machine and human-readable representations of buildings, systems, and their relationships. However, tags and tagsets in the metadata approach have some limitations that stem from a weak structure in defining entities, their properties, and relationships. In this study, we identify seven problems with the metadata approach to modelling buildings and address these problems with an object-oriented metamodel: Metamodel for Energy Things (MetamEnTh). Using an object-oriented modelling technique to establish structure and constraints, MetamEnTh produces a model that portrays a building and its systems. MetamEnTh adheres to the same naming convention of entities as other projects like Project Haystack and Brick. We accomplish a UML representation of the core structure of MetamEnTh and validate the representation through three different case studies.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".