State-of-the-Art of Digital Tools Used by Architects for Solar Design
Bibliographic record
Abstract
Until recently, building information modeling (BIM) software such as Autodesk Revit, Bentley Architecture, Graphisoft ArchiCAD, and Vectorworks Architect focused primarily on modeling and refining building geometry. Users relied on third-party software such as Green Building Studio, Ecotect, Hevacomp and IES VE to analyze the energy consumption of a building. A series of developments in the past few years (since 2008) changed that: there are many CAAD software today which include some form of connection to an energy simulation program thereby allowing passive solar gains preduction. Amongst the CAAD tools reviewed, the following BIM applications offer the most interesting possibilities for energy simulations including passive solar gains predictions: Allplan, ArchiCAD, DDS-CAD PV, MicroStation, Revit and Vectorworks. Google SketchUp, which is not a BIM application, also integrates many plugins: IES VE-Ware, OpenStudio, and Google SketchUp Demeter, which allow performing thermal simulations based on IES VE, EnergyPlus and Green Building Studio. Google SketchUp is widely recognized for being used at EDP and is often used in the architect's workflow as a predecessor software to another more complex BIM or non-BIM applications (e.g. AutoCAD).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".