Implementation of Open Source GIS Tools to Identify Bright Rooftops for Solar Photovoltaic Applications – A Case Study of Creek Lanes, DHA, Karachi
Bibliographic record
Abstract
The mega city of Karachi is still mainly dependent on conventional sources of energy to cater its daily electricity requirements. Dependence on conventional sources of energy for power production results in environmental degradation and depletion of fossil fuel resources. In particular, it also highlights an immense need of alternate sustainable solution for current electricity generation scenario. In this research work, an innovative methodology has been proposed to identify bright rooftops using open source geographic information system (GIS) tools which may be utilized for sustainable power generation in Karachi metropolis. First, bright rooftops have been extracted using open source Quantum GIS (QGIS) software. Edge extraction technique using gradient filter; an open source algorithm of QGIS has been utilized. Furthermore, image processing techniques have been used to extract and refine building rooftops. Then, rooftops have been polygonized and their area has been calculated using Measure Area function of QGIS. To assess the accuracy of the extracted rooftops, field validation work has been performed and sample rooftops have been physically measured. A comparison of extracted and physically measured sample rooftops yielded 90.45% accuracy. Reduction in total roof area has been made considering different roof uses and shading effect from nearby trees and buildings. Then, unshaded bright rooftops area of 4,626 m2 has been calculated which can be used for solar photovoltaic (PV) applications in Creek Lanes, DHA Phase 7 Karachi. An annual energy output of 2.1 MWh has been estimated using Crystalline Silicon (c-Si) solar PV panel and available rooftop area. The methodology adopted can be extrapolated to macro-scale as well. However, challenges and limitations for extrapolation of methodology have also been highlighted. Solar radiation studies that demonstrate the use of open source GIS tools for sustainable power generation for this region have been scarce. Thus, this study is a preliminary research work to highlight an immense solar electricity potential that exists for Karachi metropolis.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".