Modeling shading with the use of LiDAR for harvesting solar energy in the city of Kingston, Ontario, Canada
Levinson and Gupta (2008): Estimating solar access of typical residential rooftops: A case study in San Jose, CA presents a complete guidance for estimating the shadows cast by tress and buildings on rooftop PV systems. The process encompasses processing LiDAR data to get surface and ground elevation model (DSM and DEM), extracting urban land use classes, applying irradiation models on the features and modeling the shadows with time. If anything, this should be the core paper for the thesis. However a few modifications/ additions are forthcoming:
(i) a parallel workflow between indistrial use ArcGIS and open source GRASS LiDAR tools. The extraction of roof/ trees and calculation of their heights are further detailed in Kessner et al: Analysis of the solar potential of roofs by using official LiDAR data, Jochem et al: Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment, Clayton Crawford's tutorial and  --- to be understood and tried out by July 7th
(ii) recommended usage of the Perez et al model over the HDKR model to calculate the ration between irradiance on inclined planes to that on horizontal surfaces(with suggestions and codes from Rob and Amir (Ma and Iqbal (1983): Statistical comparison of models for estimating solar radiation on inclined surfaces, Feuermann and Zemel (1992): Validation of models for global irradiance on inclined planes, Remund et al (2003): Chain of algorithms to calculate advanced solar parameters and Duffie & Beckman (1991))
(iii) an overview of different softwares for modeling shading and solar feature (roof) extraction. the two candidates are CH2M Hill's Solar Automated Feature Extraction and the open source Radiance version 4.0 (http://www.radiance-online.org/)
(iv) the application of shading not just to PV system but also to cooling and heating loaf --> the heat island effect
Kassner et al (2008): Analysis of the solar potential of roofs by using official LiDAR data provides a concise account of analyzing the solar potential of roof, from the justification of the research to the technique used. The test area encompasses 13 buildings in the campus of the University of Cologne, Germany. The LiDAR data is filtered by way of exclusion using roof outlines as masks and then by a height threshold of 3m. Their advantage lies in the possession of high quality roof outlines, which represent the real shape and size of the roof. Our data, which is cadastral in nature, belong to the wider group. Roof planes were generated using raster interpolation and segmented using the rules of thumbs of solar PV evaluation (azimuth, aspect, tilt). They used the Hillshade function for model shading and so will we. However they did not run into the problem of separating trees and roofs, which can be explained from the fact that the test site is a campus, not residential.