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.
Ko L, Wang JC, Chen CY, Tsai HY. Evaluation of the development potential of rooftop solar photovoltaic in Taiwan. Renewable Energy 2015; 76: 582-95. http://dx.doi.org/10.1016/j.renene.2014.11.077
Naz L, Ahmad M. What inspires energy crises at the micro level: empirical evidence from energy consumption pattern of urban households from Sindh. Proceedings of The Pakistan Society of Development Economics (PSDE) 29th Annual General Meeting (AGM) and Conference; 2013: Islamabad, Pakistan. Available from: http://www.pide.org.pk/psde/ pdf/AGM29/papers/Lubna%20Naz.pdf. (Accessed: July 4, 2015).
National Aeronautics and Space Administration (NASA). Surface meteorology and solar energy (SSE) database. Available from: https://eosweb.larc.nasa.gov/sse/ (Accessed: July 4, 2015).
Liang J, Gong J, Zhou J, Ibrahim AN, Li M. An open-source 3D solar radiation model integrated with a 3D Geographic Information System. Environ Model Softw 2015; 64: 94-101. http://dx.doi.org/10.1016/j.envsoft.2014.11.019
Amesa DP, Pinthonga K, Scotta M, Khattara R, Solanb D, Leec R. Open source map based software for photovoltaic system layout design. Proceedings of 7th International Conference on Environmental Modeling and Software; 2014: San Diego, USA. Available from: http://www.iemss.org/ sites/iemss2014/papers/iemss2014_submission_152.pdf. (Accessed: July 4, 2015).
Nguyen HT, Pearce JM. Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale. Solar Energy 2015; 86(5): 1245-60. http://dx.doi.org/10.1016/j.solener.2012.01.017
Nguyen HT, Pearce JM. Estimating potential photovoltaic yield with r. sun and the open source geographical resources analysis support system. Solar Energy 2010; 84(5): 831-43. http://dx.doi.org/10.1016/j.solener.2010.02.009
Hofierka J, Ka?uk J. Assessment of photovoltaic potential in urban areas using open-source solar radiation tools. Renewable Energy 2009; 34(10): 2206-14. http://dx.doi.org/10.1016/j.renene.2009.02.021
Šúri M, Huld TA, Dunlop ED. PV-GIS: a web-based solar radiation database for the calculation of PV potential in Europe. International l Journal of Sustainable Energy 2005; 24(2); 55-67. http://dx.doi.org/10.1080/14786450512331329556
Huld T, Müller R, Gambardella A. A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy 2012; 86(6): 1803-15. http://dx.doi.org/10.1016/j.solener.2012.03.006
Rosin PL, Hervás J, Barredo JI. Remote sensing image thresholding for landslide motion detection. Proceedings of 1st Int. Workshop on Pattern Recognition Techniques in Remote Sensing 2000; 10-17.
Izquierdo S, Rodrigues M, Fueyo N. A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations. Solar Energy 2008; 82(10): 929-39. http://dx.doi.org/10.1016/j.solener.2008.03.007
Pillai IR, Banerjee R. Methodology for estimation of potential for solar water heating in a target area. Solar Energy 2007; 81(2): 162-72. http://dx.doi.org/10.1016/j.solener.2006.04.009
University of Maryland. Introduction to signal processing: signal and noise. Available from: http://terpconnect.umd.edu/ ~toh/spectrum/SignalsAndNoise.html. (Accessed: July 4, 2015.)
Balaji T, Sumathi DM. Effective features of remote sensing image classification using interactive adaptive thresholding method. arXiv preprint 2014; arXiv: 1401.7743.
Patra S, Ghosh S, Ghosh A. Histogram thresholding for unsupervised change detection of remote sensing images. Int J Remote Sens 2011; 32(21): 6071-89. http://dx.doi.org/10.1080/01431161.2010.507793
Scartezzini JL, Montavon M, Compagnon R. Computer evaluation of the solar energy potential in an urban environment. Euro Sun, Bologna 2002; Available from: http://ww.radiance-online.org/community/workshops/2002-fribourg/Compagnon/eurosun_article_scartezzini.pdf (Accessed: July 4, 2015).
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2016 Jibran Khan , Mudassar Hassan Arsalan