Identification of Building-Surrounded Obstacle Parameter Using Automated Simulation to Support Building Integrated Photovoltaic (BIPV) Layout Planning in Thailand
Bibliographic record
Abstract
The overall aim of this study was to explore the comparative effects between obstacle’s distance and obstacle’s orientation parameters that cause partially shading effects and influence the potential solar power generation of a photovoltaic (PV) system. An automatic collaboration of a BIM authoring software and a cloud-based building performance analysis tool were used to simulate the annual cumulative insolation obtained from rooftop PV surfaces of eight different orientations and forty-three different distances between the BIPV and building obstacle. Two public healthcare buildings, an OPD and a ward building that widely established throughout Thailand were our case study. This study also explores that orientation and distance of a surrounding obstacle are both important parameters that influencing the annual cumulative insolation of PV surfaces but in the different contexts. The findings of this study also support decision making for BIPV designers and planners to acknowledge which the BIPV and the obstacle placement is highly effective, and which one is encountering a problem and its solution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".