An Automated Space-Based Graph Generation Framework for Building Energy Consumption Estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The 3D information in Building Information Modeling (BIM) has received significant interest for smart city applications. Recently, employing Industry Foundation Classes (IFC) for BIM in data-driven methods for Building Energy Consumption Estimation (BECE) has gained momentum because of the enriched geometric and semantic information. However, despite extensive studies on applying the IFC data in BECE analysis, employing the full potential of the BIM remains poor due to its complex data model and incompatibility with data-driven algorithms. This paper proposes a framework to extract accurate semantic, geometry, and topology information from the room-level (space) IFC schema by introducing new geo-computation algorithms to deal with these challenges. Additionally, we define a new topological weighted relationship between spaces in different stories by combining common geometry area with energy resistance value. Eventually, the proposed weighted space-based graph will be constructed to decrease the original complexity of the IFC model, and it is compatible with graph-based machine learning algorithms. The results are promising, with more than 90% accuracy in extracting the geometry information for the convex and non-convex polyhedron rooms and 100% accuracy in detecting vertical and horizontal adjacent rooms. This study confirms the proposed approach’s efficiency, accuracy, and feasibility for space-based BECE analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it