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Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes

Authors :
Xiu Lu
Guannan Li
Andong Wang
Qingqin Xiong
Bingxian Lin
Guonian Lv
Source :
ISPRS International Journal of Geo-Information, Vol 10, Iss 12, p 827 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Photovoltaic energy generation has gained wide attention owing to its efficiency and environmental benefits. Therefore, it has become important to accurately evaluate the photovoltaic energy generation potential of building surfaces. As the number of building floors increases, the area of the facades becomes much larger than that of the roof, providing improved potential for photovoltaic equipment installation. Conventional urban solar potential evaluation methods are usually based on light detection and ranging (LiDAR). However, LiDAR can only be used in existing buildings, and the lack of semantic information in the point cloud data generated by LiDAR makes it impossible to evaluate the photovoltaic potential of facades (including details such as windows) in detail and with accuracy. In this study, we developed a method to accurately extract facades and roofs in order to evaluate photovoltaic potential based on the Industry Foundation Classes. To verify the feasibility of this approach, we used a building from Xuzhou city, Jiangsu province, China. The simulation results indicate that, out of the total building photovoltaic installable area (8995 m2), that of the facade is 8240 m2. The photovoltaic potential of the simulated building could reach 1054.69 MWh/year. The sensitivity studies of the grid resolution, the time interval and the computation time confirmed the reasonability of the determined conditions. The method proposed offers great potential for energy planning departments and the improved utilization of buildings.

Details

Language :
English
ISSN :
22209964
Volume :
10
Issue :
12
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.0e2a448381634a76b271953f1f88d914
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi10120827