In this paper, a methodology for estimating solar potential on multiple building rooftops is presented.
The objective of this methodology is to estimate the daily or monthly solar energy on individual buildings
in a city/region using light detection and ranging (LiDAR) data and a geographic information system
(GIS) approach. Conceptually, the methodology is based on the upward-looking hemispherical viewshed
algorithm, but applied using an area-based modeling approach. The methodology considers input parameters,
such as surface orientation, shadowing effect, elevation, and atmospheric conditions that influence
solar intensity on the earth surface. The methodology has been implemented for some 212,000 buildings
in Knox County, Tennessee, USA. Based on the results obtained, the methodology seems to be adequate
for estimating solar radiation on multiple building rooftops. The use of LiDAR data improves the radiation
potential estimates in terms of the model predictive error and the spatial pattern of the model outputs.
This methodology could help cities/regions interested in sustainable projects to quickly identify buildings
with higher potentials for roof-mounted photovoltaic systems.
In this paper, a methodology for estimating solar potential on multiple building rooftops is presented.The objective of this methodology is to estimate the daily or monthly solar energy on individual buildingsin a city/region using light detection and ranging (LiDAR) data and a geographic information system(GIS) approach. Conceptually, the methodology is based on the upward-looking hemispherical viewshedalgorithm, but applied using an area-based modeling approach. The methodology considers input parameters,such as surface orientation, shadowing effect, elevation, and atmospheric conditions that influencesolar intensity on the earth surface. The methodology has been implemented for some 212,000 buildingsin Knox County, Tennessee, USA. Based on the results obtained, the methodology seems to be adequatefor estimating solar radiation on multiple building rooftops. The use of LiDAR data improves the radiationpotential estimates in terms of the model predictive error and the spatial pattern of the model outputs.This methodology could help cities/regions interested in sustainable projects to quickly identify buildingswith higher potentials for roof-mounted photovoltaic systems.
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