BIM-based Surface-Specific Solar Simulation of Buildings
Bibliographic record
Abstract
Photovoltaic (PV) solar energy is rapidly growing as an attractive alternative to fossil fuels. PV panels can harvest the solar power and turn it into a clean source of electricity. Traditionally, PV panels are only used on the rooftops of buildings. However, with the emergence of building-integrated solar panels in recent years, other surfaces on the building façade can be considered for the installation of PV panels. Given that different panels have different cost and performance profiles, it is of a cardinal importance to properly design the PV panels on the building facades to ensure a maximum benefit-cost ratio. Existing simulation and optimization methods do not discriminate between different types of surfaces of the building and treat the building envelope as a set of polygons. This can result in under- or over- design since there is a strong relationship between the type of the surfaces and the type of PV panels that can be attached to them or integrated with them. The advent of Building Information Modeling (BIM) in recent years has provided a rich platform for object-based evaluation and analysis of buildings. Nonetheless, currently, BIM is not used for a detailed and surface-specific simulation of building surfaces. In this research, a BIM-based method is developed for a detailed simulation of a building envelope using its surface properties. A prototype is developed using Dynamo visual programming platform to demonstrate the feasibility of the proposed method, and a case study is presented for a building in Montreal, Canada. In the light of the result of the case study, it can be concluded that the proposed method is promising in terms of providing the input for a comprehensive planning of the solar panel layout.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".