Estimating Geographical PV Potential Using LiDAR Data for Buildings in Downtown San Francisco
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Bibliographic record
Abstract
Abstract Sustainable solar energy is of the interest for the city of S an F rancisco to meet their renewable energy initiative. Buildings in the downtown area are expected to have great photovoltaic ( PV ) potential for future solar panel installation. This study presents a comprehensive method for estimating geographical PV potential using remote sensed LiDAR data for buildings in downtown S an F rancisco. LiDAR derived DSM s and DTM s were able to generate high quality building footprints using the object‐oriented classification method. The GRASS built‐in solar irradiation model ( r.sun ) was used to simulate and compute PV yields. Monthly and yearly maps, as well as an exquisite 3 D city building model, were created to visualize the variability of solar irradiation across the study area. Results showed that monthly sum of solar irradiation followed a one‐year cycle with the peak in July and troughs in J anuary and D ecember. The mean yearly sum of solar irradiation for the buildings in the study area was estimated to be 1675 kWh/m 2 . A multiple regression model was used to test the significance of building height, roof area and roof complexity against PV potential. Roof complexity was found to be the dominant determinant. Uncertainties of the research are mainly from the inherent r.sun limitations, boundary problems, and the LiDAR data accuracy in terms of both building footprint extraction and 3 D modeling. Future work can focus on a more automated process and segment rooftops of buildings to achieve more accurate estimation of PV potential. The outcome of this research can assist decision makers in S an F rancisco to visualize building PV potential, and further select ideal places to install PV systems. The methodology presented and tested in this research can also be generalized to other cities in order to meet contemporary society's need for renewable energy.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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