High-Level Framework for GIS-Based Optimization of Building Photovoltaic Potential at Urban Scale Using BIM and LiDAR
Bibliographic record
Abstract
According to the ascending worldwide energy consumption records and limitations of fossil energy sources, it is important to exploit more sustainable resources. Photovoltaic (PV) solar energy is one of the most promising renewable energy sources. Based on International Energy Agency (IEA) analysis, 20–25% of the world electricity supply will be PV-based by 2050. Generating electricity from PV panels installed on buildings’ surfaces provides safe and silent options for onsite distributed power generation, and reduces energy transmission losses. Regarding the high volume of buildings in the urban area, a primary step for implementing a PV system is assessing the solar radiation potential on the building’s surfaces and excluding the unfeasible surfaces for harvesting the solar power considering the shadow effects and obstructions. A detailed and updated geometry model of the building is another important requirement. Various studies investigated the usage of light detection and ranging (LiDAR) technology to evaluate solar potential on rooftops and facades. However, these studies did not fully capture small objects on rooftops, such as chimneys, dormers, and air conditioning systems. In addition, architectural details of building facades (e.g., windows and balconies) are mostly ignored. On the other hand, building information models (BIM) provide valuable data about the design of buildings. Combining the captured point cloud with BIM is a complementary approach to improve the building model. Considering the obtained information about the buildings, this study is going to develop the optimization module at two levels. First, optimizing PV panels’ location on buildings’ surfaces to maximize solar radiation, and then optimizing the size, number, and layout of the PV panels to maximize the panel capacity and to achieve the maximum energy generation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".