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Shading modeling using LIDAR/ GIS for the purpose of greening the city grid of Kingston, ON, Canada
{{QASpage}}


The following outline is going to be updated so as to suit the title.
'''Modeling shading with the use of LiDAR for harvesting solar energy in the city of Kingston,    Ontario, Canada'''


== Chapter 1 ==
[http://escholarship.org/uc/item/0gr843sm?display=all 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:
What is a distributed energy system?
Benefits of such a system for cities in Canada (drawn from as many available and applicable locations as possible e.g. a city in Ontario vs. a First Nation community in Nunavut)


== Chapter 2 ==
(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 [http://www.isprs.org/proceedings/XXXVII/congress/4_pdf/70.pdf Kessner et al: Analysis of the solar potential of roofs by using official LiDAR data], [http://www.mdpi.com/1424-8220/9/7/5241/pdf Jochem et al: Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment], [http://blogs.esri.com/Dev/blogs/geoprocessing/archive/2008/11/06/Lidar-Solutions-in-ArcGIS_5F00_part-1_3A00_-Assessing-Lidar-Coverage-and-Sample-Density.aspx Clayton Crawford's tutorial] and [http://grass.osgeo.org/wiki/LIDAR] --- to be understood and tried out by July 7th
Background in solar energy measurement
Solar energy potential for Southeastern Ontario


== Chapter 3 ==
(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 ([http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V50-497SPM2-5B&_user=1025668&_coverDate=12%2F31%2F1983&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1392322122&_rerunOrigin=google&_acct=C000050549&_version=1&_urlVersion=0&_userid=1025668&md5=cbe3ded8651ba94e024c50daa2a1c33b Ma and Iqbal (1983): Statistical comparison of models for estimating solar radiation on inclined surfaces], [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V50-498029F-MV&_user=1025668&_coverDate=12%2F31%2F1992&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1392324781&_rerunOrigin=google&_acct=C000050549&_version=1&_urlVersion=0&_userid=1025668&md5=e9291f3c4a85471e278acc1ebebe2958 Feuermann and Zemel (1992): Validation of models for global irradiance on inclined planes], [http://hal.archives-ouvertes.fr/docs/00/46/57/92/PDF/ises2003_chain.pdf Remund et al (2003): Chain of algorithms to calculate advanced solar parameters] and Duffie & Beckman (1991))
Background on integrating PV in urban area
 
(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
 
[http://www.isprs.org/proceedings/XXXVII/congress/4_pdf/70.pdf 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.
 
[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V50-4X7BJCR-1&_user=1025668&_coverDate=12%2F31%2F2009&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1392282206&_rerunOrigin=google&_acct=C000050549&_version=1&_urlVersion=0&_userid=1025668&md5=96e003d7d27d9883374667bbb92e1ff4 Levinson et al (2009): Solar access of residential rooftops in four California cities]
 
[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V50-4JGJG1F-1&_user=1025668&_coverDate=12%2F31%2F2006&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1392292546&_rerunOrigin=google&_acct=C000050549&_version=1&_urlVersion=0&_userid=1025668&md5=84019ba3c26e85c6befe36c3592a7434#bbib10 Robinson (2006): Urban morphology and indicators of radiation availability]
 
[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V2V-4C4BN7S-1&_user=1025668&_coverDate=04%2F30%2F2004&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1392293854&_rerunOrigin=google&_acct=C000050549&_version=1&_urlVersion=0&_userid=1025668&md5=2c8b189b64c196631701ce3ff2457d6e Compagnon (2004): Solar and daylight availability in the urban fabric]


[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V2P-4R34DMS-1&_user=10&_origUdi=B6V50-4DVB9PG-1&_fmt=high&_coverDate=05%2F31%2F2008&_rdoc=1&_orig=article&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=f85f7cbd14db3ff23b1619fe110a093e Ruther et al (2004): Potential of building integrated photovoltaics solar energy generators in assisting daytime peaking feeders in urban areas in Brazil]
[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V2P-4R34DMS-1&_user=10&_origUdi=B6V50-4DVB9PG-1&_fmt=high&_coverDate=05%2F31%2F2008&_rdoc=1&_orig=article&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=f85f7cbd14db3ff23b1619fe110a093e Ruther et al (2004): Potential of building integrated photovoltaics solar energy generators in assisting daytime peaking feeders in urban areas in Brazil]
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[http://www.kurochans.net/paper/pvsec/suzuki6DV.4.52.pdf Suzuki, Ito and Kurosawa (2007): An analysis of PV resource in residential areas by means of aerial photo images]
[http://www.kurochans.net/paper/pvsec/suzuki6DV.4.52.pdf Suzuki, Ito and Kurosawa (2007): An analysis of PV resource in residential areas by means of aerial photo images]


== Chapter 4 ==
[http://www.springerlink.com/content/h747x6l0613t26l5/ David et al (2009): Solar Resource Assessment for PV applications]
Solar energy potential for buidings (owned by the city of Kingston - part I of the internship and extrapolated from Lindsay's paper from the summer of 2009)
 
== Chapter 5 ==
Integration of solar energy into the city grid (essentially part II of the internship)
 
== Chapter 6 ==
The economic and social dimension of the project
The degree of decentralization afforded by the city grid


== Chapter 7 ==
If combined with ground mounted PV, what is the per capita solar footprint for Kingston/ Southern Ontario?
How does this project improve the sustainability of the city
Lessons/ experience for other cities in Ontario/ Canada


[[Category: Queens Applied Sustainability Group]]
[[Category:Queens Applied Sustainability Group Literature Reviews]]
[[Category: Photovoltaics]]

Revision as of 02:35, 15 January 2011

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 [1] --- 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.

Levinson et al (2009): Solar access of residential rooftops in four California cities

Robinson (2006): Urban morphology and indicators of radiation availability

Compagnon (2004): Solar and daylight availability in the urban fabric

Ruther et al (2004): Potential of building integrated photovoltaics solar energy generators in assisting daytime peaking feeders in urban areas in Brazil

Castro et al (2004): Grid-connected PV buildings: analysis of future scenarios with an example of Southern Spain

Rae et al (1999): Estimating the uptake of distributed energy in an urban setting

Denholm and Margolis (2008): Land use requirements and the per capita solar footprint for PV generation in the US

Levinson and Gupta (2008): Estimating solar access of typical residential rooftops: A case study in San Jose, CA

Ayoub, Dignard-Bailey and Fillion (2000): Photovoltaics for buildings: Opportunities for Canada

Pelland and Poisson (2008): An Evaluation of the Potential of Building Integrated Photovoltaics in Canada

Suzuki, Ito and Kurosawa (2007): An analysis of PV resource in residential areas by means of aerial photo images

David et al (2009): Solar Resource Assessment for PV applications

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