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ISPRS Int. J. Geo-Inf. 2017, 6(4), 106; doi:10.3390/ijgi6040106

A Sparse Voxel Octree-Based Framework for Computing Solar Radiation Using 3D City Models

1,2,3,* and 1,3,*
1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
School of Life Sciences, Arizona State University, Tempe 85287, AZ, USA
3
Zhejiang-CAS Application Center for Geoinformatics, Jiashan 314100, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 19 February 2017 / Revised: 17 March 2017 / Accepted: 29 March 2017 / Published: 31 March 2017
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Abstract

An effective three-dimensional (3D) data representation is required to assess the spatial distribution of the photovoltaic potential over urban building roofs and facades using 3D city models. Voxels have long been used as a spatial data representation, but practical applications of the voxel representation have been limited compared with rasters in traditional two-dimensional (2D) geographic information systems (GIS). We propose to use sparse voxel octree (SVO) as a data representation to extend the GRASS GIS r.sun solar radiation model from 2D to 3D. The GRASS GIS r.sun model is nested in an SVO-based computing framework. The presented 3D solar radiation computing framework was applied to 3D building groups of different geometric complexities to demonstrate its efficiency and scalability. We presented a method to explicitly compute diffuse shading losses in r.sun, and found that diffuse shading losses can reduce up to 10% of the annual global radiation under clear sky conditions. Hence, diffuse shading losses are of significant importance especially in complex urban environments. View Full-Text
Keywords: 3D solar radiation model; photovoltaic energy; 3D city models; sparse voxel octree 3D solar radiation model; photovoltaic energy; 3D city models; sparse voxel octree
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Liang, J.; Gong, J. A Sparse Voxel Octree-Based Framework for Computing Solar Radiation Using 3D City Models. ISPRS Int. J. Geo-Inf. 2017, 6, 106.

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