GIS-Based Estimation of Potential Solar Energy on Flat Roofs in Maadi, Cairo, using True Ortho World View Image and Digital Surface Model

Abstract

This research is concerned with the use of remotely sensed data in the estimation of the solar energy potentiality of the flat roofs in El Maadi District, Cairo. A solar radiation model was applied using a digital surface model (DSM) generated from two Worldview stereo satellite images using the digital photogrammetric workstation (Leica Photogrammetric Suite) LPS. The procedures included applying orientation with Rational Polynomial Coefficients (RPC) with and without GCPs, tie point measurements, aerial triangulation, automatic DSM and editing, true ortho-rectification. The accuracy of the DSM has been assessed and reported for both methods. The ortho-rectification, of stereo satellite images has been performed. A PAN true ortho-images with 0.5 m resolution resulted from applying orientation with rational polynomial coefficients (RPCs) refined with four accurate differential ground control points (DGCPs) and a high accurate DSM derived from stereo images were generated. Two classification methods were conducted (SVM and ANN) on the true ortho-image. First method without utilizing texture, the second utilizing texture features of Grey Level Co-occurrence Matrix (GLCM) as inputs. The SVM of the ortho-image and texture features overall accuracy was 93.4%, kappa coefficient was 0.92 and ANN of ortho-image and texture features overall accuracy of 91.2% and kappa coefficient was 0.90. Using the generated DSM as input, the area solar radiation model in ESRI ArcGIS was run. The model resulted in an annual total radiation grid for year 2015 which was converted to average daily radiation in kilowatt hour/square meters per day. A sample of 265 buildings footprints was extracted from the classification and used for the estimation. Assuming that 50% roof areas are available for solar Photovoltaic (PV), 12% PV grid conversion efficiency and 0.6 performance ratio and using ESRI zonal statistics tool, the technical potentiality for solar PV electricity generation for the sample buildings was calculated, classified into five classes and mapped.