Paper Info
Title
Estimating Geographical PV Potential Using LiDAR Data for Buildings in Downtown San Francisco
Abstract
Sustainable solar energy is of the interest for the city of San Francisco to meet their renewable energy initiative. Buildings in the downtown area are expected to have great photovoltaic (PV) potential for future solar panel installation. This study presents a comprehensive method for estimating geographical PV potential using remote sensed LiDAR data for buildings in downtown San Francisco. LiDAR derived DSMs and DTMs were able to generate high quality building footprints using the object-oriented classification method. The GRASS built-in solar irradiation model (r.sun) was used to simulate and compute PV yields. Monthly and yearly maps, as well as an exquisite 3D city building model, were created to visualize the variability of solar irradiation across the study area. Results showed that monthly sum of solar irradiation followed a one-year cycle with the peak in July and troughs in January and December. The mean yearly sum of solar irradiation for the buildings in the study area was estimated to be 1675 kWh/m(2). A multiple regression model was used to test the significance of building height, roof area and roof complexity against PV potential. Roof complexity was found to be the dominant determinant. Uncertainties of the research are mainly from the inherent r.sun limitations, boundary problems, and the LiDAR data accuracy in terms of both building footprint extraction and 3D modeling. Future work can focus on a more automated process and segment rooftops of buildings to achieve more accurate estimation of PV potential. The outcome of this research can assist decision makers in San Francisco to visualize building PV potential, and further select ideal places to install PV systems. The methodology presented and tested in this research can also be generalized to other cities in order to meet contemporary society's need for renewable energy.
Year
DOI
Venue
2015
10.1111/tgis.12140
TRANSACTIONS IN GIS
Field
DocType
Volume
Renewable energy,Computer science,Remote sensing,Solar energy,Downtown,Building model,Lidar,Roof,Footprint,Photovoltaic system
Journal
19.0
Issue
ISSN
Citations 
6.0
1361-1682
4
PageRank 
References 
Authors
0.41
8
3
Name
Order
Citations
PageRank
Ziqi Li1132.42
Zidong Zhang240.41
Keith Davey340.41