Integrating GIS and BIM for Community-Scale Energy Modeling
Bibliographic record
Abstract
To achieve energy-efficient design in urban communities, the design phase needs to adopt reliable energy modeling approaches. However, current urban modeling approaches often use abstract and low level information to describe buildings because of the difficulties of collecting and managing building data on the large scale required of such urban communities. This abstraction of building data creates large uncertainties in the modeling and simulation of energy scenarios at the community level. An important part of the solution to this challenge relies on the integration of information systems at the scale of both urban communities and individual buildings, which are based on geographic information system (GIS) and building information modeling (BIM) respectively. Since current technologies do not sufficiently address the interoperability between GIS and BIM, the existing conversion between GIS and BIM does not satisfy the data requirements for community energy design. This paper investigates this challenge and presents an approach that uses semantic web technologies, including web ontology language (OWL) and resource description framework (RDF), to integrate GIS and BIM data. In this approach, we use an extract, transform and load (ETL) tool to convert GIS and BIM data to RDF and conduct queries on the integrated RDF to provide the required information for energy simulation. The approach is tested through a case study of the University of British Columbia campus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".