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Estimating Geographical PV Potential Using LiDAR Data for Buildings in Downtown San Francisco
Author(s) -
Li Ziqi,
Zhang Zidong,
Davey Keith
Publication year - 2015
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12140
Subject(s) - downtown , roof , footprint , lidar , photovoltaic system , renewable energy , environmental science , meteorology , remote sensing , solar energy , geography , civil engineering , engineering , electrical engineering , archaeology
Sustainable solar energy is of the interest for the city of S an F rancisco 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 S an F rancisco. LiDAR derived DSM s and DTM s 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 3 D 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 J anuary and D ecember. 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 3 D 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 S an F rancisco 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.