2.7 Surface regeneration
Only the surfaces where at least one subdivided triangle is
shadowed are considered for surface regeneration. This step is
only necessary when a visual output for real time shadow is
required for an instance of time. If the calculation is carried out
for any longer time period like and hourly or minutely shadow
calculation then this step might be excluded. Neighbouring
subdivided triangles with same shadow status are joined
together to form shadow and non-shadow region. These regions
are further merged with other neighbouring region with similar
shadow status.
The whole process produces result for an instance of time. To
get hourly or minutely shadow calculation this has been
repeatedly applied and the result has been presented in a tabular
form.
3. IMPLEMENTATION
To implement this method a specific area in Germany was
considered. Former military area Scharnhauser Park shown in
Figure 3 is an urban conversion and development area of 150
hectors in the community of Ostfildern on the southern border
near Stuttgart with 7000 inhabitants. About 80% heating energy
demand of the whole area is supplied by renewable energies and
a small portion of electricity is delivered by existing roof top
photovoltaic system (Tereci et al, 2009). This has been selected
as the study area for this research because of availability
CityGML and LIDAR data, building footprints and existing
photovoltaic cells on roofs and façades. Two types of data have
been considered for this research, CityGML and LIDAR for
accuracy. Line plane intersection method has been used initially
for direct radiation. LIDAR points are suitable for selecting
points on a surface from which the suns direction represents the
line. For CityGML data these points needs to be extracted using
surface subdivision and triangulation. But planes are
represented by facesets in CityGML, which is also need for the
algorithm. LIDAR points are needed to be converted into planes
using surface reconstruction. So, both types of data are potential
for this research.
Figure 3. Residential area Scharnhauser Park (Eicker et al,
2010)
An existing method for photovoltaic potentiality analysis which
doesn’t consider shadow has been selected as a starting point.
Solarcity3D is a student project from University of Applied
Sciences Stuttgart. This is a Software Interface for the
calculation and visualization of photovoltaic potential in city
areas. It connects 3D city models and simulation engines for the
calculation and visualization of potential energy yields.
Parameters considered here are geographical position and
orientation, roof area and pitch, climate conditions etc
(Solarcity3D, 2009). This will be modified and fast and exact
shadow detection method will be added and effect will be
determined during this research as well as the guideline for
minimum quality of data required for the calculation will also
be prepared.SolarCity3D is the base of this research so the
shadow calculation has been integrated with its system
architecture.
3D Viewer CATsEyE
INSEL CSV l !
Simulation s
Engine »| SolarCity3D CSV = Shadow
= Calculation
heer
: : :
Sim
results CATS
LT ER
EXCEL 3D urban model | | ———
{City GML) Ë Spatial DB
Es i (MySQL)
= el
Le "EE A e
Figure 4. System Architecture (Alam et al, 2011)
For shadow calculation geometrical information is needed as
input and semantic information serves the simulation tool.
Citymodel Administration Toolkit (CAT3D) has been used, to
handle, manage and merge different formats of 3D Geodata,
DBMS, and data schema on the server side. It provides data in
different layers, which can group objects thematically for clients
to query models according to needs. CAT3D framework is
divided into four parts: Data Connectors, Data Format Creators,
Data Mapping and Utilities (Knapp et al, 2007). Shadow
calculator gets the information from CAT3D framework and
performs shadow calculation at specified time interval (per day,
hour, minute) and prepares a CSV file with shadow information
for each building. This file is feed into the SolarCity3D for solar
potentiality analysis with the simulation engine INSEL. A 3D
viewer is used for classified visualization of the results. And the
final potentiality result with shadow consideration is also
presented in excel file. Figure 4 represents the whole system
architecture.
4. RESULT & ANALYSIS
Initially to check the algorithm a test model with two buildings
as shown in Figure 5, have been tested one of which has a
photovoltaic module on its roof.
Figure 5. Implementation on a test model
The model has been tested for each hour of the year, which
means 8760 times. The test has given correct tabular result
which is
that if tl
will be 11
Then the
most of !
early in |
Figure 7
in red g
The in
where t
product
weekes
Figure
cells in
signific
module
or teler
city m
existing
Figure