ASSESMENT OF SOLAR POTENTIAL ROOFTOPS USING GIS AND REMOTE SENSING TECHNIQUES

ASSESSMENT OF SOLAR POTENTIAL ROOFTOPS BY USING
GIS AND REMOTE SENSING TECHNIQUES
By
M.R. RIFAS AHAMED
FG 496
This dissertation submitted to the Faculty of Geomatics, Sabaragamuwa University of
Sri Lanka in partial fulfilment of the requirement for the degree in
B.Sc. SURVEYING SCIENCES
DEPARTMENT OF REMOTE SENSING AND GIS
FACULTY OF GEOMATICS
SABARAGAMUWA UNIVERSITY OF SRI LANKA
2017

i
DISCLAIMER
This document describes work undertaken as a part of programme of study at the Faculty
of Geomatics, Sabaragamuwa University of Sri Lanka. All views and opinions expressed
therein remain the sole of responsibility of the author, and do not necessarily represent
those of the institute.

ii
DECLARATION
I do hereby declare that the work reported in this dissertation was exclusively carried out
by me under the supervision of Dr G.S.N. Perera. This Research project report represents
my own work except where due acknowledgement is made, and it has not been previously
included in a thesis, dissertation, or report submitted to the Sabaragamuwa University of
Sri Lanka or to any other institution for a degree, diploma or other qualification.
Mr. M.R. Rifas Ahamed
Signature: ………………… Date: …………………..
Certified by:
Supervisor: - Dr G.S.N. Perera
Signature: ............................. Date: .............................
Signature of Dean
………………………. Date: ………………….

iii
ABSTRACT
Solar energy enables to replace conventional or non-renewable energy sources. This
scenario has been widely implemented all over the world these days. Generally solar
harvesting plants are constructed by demolishing vast vegetation land parcels or forest
covers. But utilizing rooftops is known as feasible and trending one. But before installing
solar panels over rooftops, assessing where solar panels should be placed is an important
factor. This can be significantly benefited panel performance.
Currently, one of the most challenging problem which makes notable impact on efficiency
of producing solar energy is casting shadow by other objects such as buildings besides to
respective rooftops. GIS and Remote Sensing techniques were addressed in great level to
figure out optimum solution by excluding shadow casted sectors. Study consists of two
phases. First one is modeling 3D buildings and simulate shadow over model. And following
one is spatial analysis over temporal shadow simulation models with solar data. 3D
modelling with SketchUp and the integration of geospatial techniques (spatial analysis)
were pillars of present work processes. Especially, SketchUp was well customized and
resourceful for most required factors of study such accurate sun movement, position and
angle. Frequent weather imbalance of day and seasonal changes over year were considered
by analyzing annual solar data (i.e. Global Horizontal Insolation). By hourly casted shadow
raster models of a day, were got into spatial analysis (i.e. weighted overlay) with ArcGIS
desktop. Resultant of process was rooftop solar potential map. By assessing this, users
would be able to find out right solar potential spots over rooftops.

iv
ACKNOWLEDGEMENT
I am using this opportunity to express my gratitude to everyone who supported me
throughout the Research project. I am thankful for their aspiring guidance, invaluably
constructive criticism and friendly advice during the research work. I am sincerely grateful
to them for sharing their truthful and illuminating views on a number of issues related to
the research project.
First of all, I would like to thank Prof. Chandana P.Udawatta, the Vice Chancellor of
Sabaragamuwa University of Sri Lanka and Dr. H.M.I. Prasanna, Dean of Faculty of
Geomatics.
Specially, my heartiest thanks must goes to my Superviser Dr. G.S.N. Perera, head of the
department of Remote Sensing and GIS and Senior lecturer, Faculty of Geomatics,
Sabaragamuwa University of Sri Lanka, who was completely involved in the research
project, by providing guidance and correcting me throughout the study with attention, care,
knowledge, and inspecting the work, helping to organize the process well.
And Dr. D.R. Wellikkanna, head of the department of Surveying and Geodesy and the
coordinator of the research project 2017, gave great effort by supplying necessary
arrangements to carry out this task successfully.
And thanks to my respective parents and friends who were helped with the research and
supported to complete it successfully.

v
TABLE OF CONTENTS
DISCLAIMER ..................................................................................................................i
DECLARATION .............................................................................................................ii
ABSTRACT....................................................................................................................iii
ACKNOWLEDGEMENT..............................................................................................iv
TABLE OF CONTENTS.................................................................................................v
LIST OF TABLES........................................................................................................viii
LIST OF FIGURES ........................................................................................................ix
LIST OF ACCRONYMS.................................................................................................x
Chapter 1..........................................................................................................1
1.0 INTRODUCTION .....................................................................................................1
1.1 Background............................................................................................................1
1.2 Energy Demand Crisis and Favorable National Energy Policies ..........................2
1.2.1 Experiencing Energy Demand Growth...............................................................2
1.2.2 National Energy Policies.....................................................................................3
1.3 Problem Statement.................................................................................................3
1.4 Research Objective ................................................................................................4
1.4.1 Major Objective ..................................................................................................4
1.4.2 Minor Objective..................................................................................................4
1.5 Scope and Limitation.............................................................................................4
1.5.1. Scope..................................................................................................................4
1.5.2 Limitation............................................................................................................4
Chapter 2..........................................................................................................5
2.0 THEORETICAL BACKROUND..............................................................................5
2.1 Literature review....................................................................................................5
2.2 Factor influencing in site selection ........................................................................5

vi
2.2.1 Environmental aspect..........................................................................................6
2.2.1.1 Land use type ...................................................................................................6
2.2.1.2 Amount of sun light exposure and seasonal changes over year.......................6
2.2.2 Social and cultural aspects..................................................................................7
2.2.2.1 Building............................................................................................................7
2.2.2.2 Energy shortage crisis and in seek of alternative way of source .....................7
2.2.2.3 Environmental friendly....................................................................................7
2.3 Significant usage of 3D models in GIS..................................................................8
2.3.1 3D building models.............................................................................................8
2.3.2 Criteria to be considered in 3D building model..................................................8
2.3.2.1 Utilization of accurate height...........................................................................8
2.3.2.2 Accurate geo location ......................................................................................9
2.4 Estimation of solar potential..................................................................................9
2.4.1 Existing techniques used to figured out..............................................................9
2.4.1.1 Constant-value methods...................................................................................9
2.4.1.2 Manual selection methods..............................................................................10
2.4.1.3 GIS-based methods........................................................................................10
2.4.2 Variance of present study from existing ones...................................................11
2.4.3 Considerable factors on modeling and analyzing .............................................11
2.4.3.1 Application used to design solar potential.....................................................11
2.4.3.2 Solar radiation data ........................................................................................12
Chapter 3........................................................................................................14
3.0 METHEDOLOGY...................................................................................................14
3.1 Description of study area .....................................................................................14
3.2 Data......................................................................................................................14
3.3 Research design ...................................................................................................15
3.3.1 Generation of geo-located 3d building models.................................................16

vii
3.3.2 Shadow casted rooftop raster models ...............................................................16
3.3.3 Process of solar data for weight assignment.....................................................16
3.3.4 Spatial analysis over shadow casted raster models...........................................17
Chapter 4........................................................................................................18
4.0 RESULTS AND DISCUSSION..............................................................................18
4.1 geo- located 3D building model...........................................................................18
4.2 Multiple type of rooftop model............................................................................19
4.4 Shadow casted building models...........................................................................19
4.5 Reclassified shadow casted raster models ...........................................................21
4.6 Weighted overlay analysis ...................................................................................23
4.6.1 Weight assignment............................................................................................23
4.7 Solar potential rooftop model ..............................................................................27
Chapter 5........................................................................................................30
5.0 CONCLUSION AND RECOMMENDATION.......................................................30
5.1 Conclusion ...........................................................................................................30
5.2 Fulfilment of proposed study...............................................................................30
5.3 Future research.....................................................................................................31
5.3.1 Implementation of geo-portal hubs in solar potential.......................................31
5.3.2 Establishment of solar electric filling stations..................................................31
5.4 Recommendation .................................................................................................32
6.0 REFERENCES ........................................................................................................33

viii
LIST OF TABLES
Table 1: Solar data processing……………………………………………………...24

ix
LIST OF FIGURES
Figure 1: Variation of shadow casts for actual and arbitrary height inputs.........................9
Figure 2: Variation shadow casts for geo-located and non-geo-located model...................9
Figure 3: GHI value dispersion over Island.......................................................................13
Figure 4: Study Area..........................................................................................................14
Figure 5: Research design..................................................................................................15
Figure 6: Geo-located 3D building model .........................................................................18
Figure 7 Various type of rooftops......................................................................................19
Figure 8: Temporal shadow casting model over rooftop (Perspective view) ....................20
Figure 9: Temporal shadow casting model over rooftop (Orthographic view) .................21
Figure 10: Reclassified shadow casted raster models........................................................22
Figure 11: Model builder of spatial analysis......................................................................24
Figure 12: Assigned weights in weighted overlay analysis...............................................25
Figure 13: Monthly hour basis solar radiation...................................................................26
Figure 14: Hour basis solar radiation of a day...................................................................26
Figure 15: Derived weights................................................................................................27
Figure 16: Solar potential rooftop map..............................................................................28
Figure 17: Zoomed view of solar potential rooftop map...................................................29
Figure 18: Solar electric power filling station ...................................................................32

x
LIST OF ACCRONYMS
BIM -Building Information Modelling
DHI -Direct Horizontal Insolation
DNI -Direct Normal Insolation
GHI -Global Horizontal Insolation
GIS -Geographical Information System
PV -Photo Voltaic
RS -Remote Sensing
VHR -Very High Resolution

1
Chapter 1
1.0 INTRODUCTION
1.1 Background
Energy is fundamental to the quality of our lives. Cooking a dinner, heating a house,
lighting a street, keeping a hospital open, running a factory – all these require energy.
Nowadays, we are very dependent on an abundant and uninterrupted supply of energy for
living and working. It is a key ingredient in all sectors of modern economies. We know that
energy demand will increase significantly in the future. This because of exceeding global
population and energy needs increase hand-in-hand and the current fossil-fuel based energy
system is not sustainable as it contributes substantially to climate change and depends
heavily on imports from very few countries. In fact, this research is addressing on how this
energy crisis issues would be satisfied in a manner of environmentally friendly way. Now
a days, world of science and people’s curiosity are trying to figure out environmental
friendly, in the other words renewable energy sources. Renewable energy is energy that is
generated from natural processes that are continuously replenished. This includes sunlight,
geothermal heat, wind, tides, water, and various forms of biomass. This energy cannot be
exhausted and is constantly renewed. In addition, environmentalist and policy makers are
trying to reduce the usage of fossil fuel energy sources by replacing with renewable energy.
This action would give better answers for issues such as global warming and environmental
imbalance, which are experienced, by current world.
Especially Solar energy, which is being as world trend now, an abundant energy source. In
fact, this exists so long before the discovery of petroleum. Ordinarily, this resource is
harvested by acquiring vast land for installing solar panels in rural area. By doing this, a
huge area of vegetation land would be demolished. However, as considering urban area,
which accumulated with many buildings, could be utilized by using building roof-tops.
Regarding to this research, most of areas in Colombo, Sri Lanka are being in similar
condition as above stated. Even more Colombo is not only concentrated with buildings but
also with over population density and vehicular movements. As being with high population

2
density and industrial zones, more electrical power requirements is experienced now.
Because of long line vehicular movements and machinery usage in industries, high level of
GHGs emission is also there. These excessive GHGs emissions cause to global warming.
By replacing these kind of vehicles by electrical oriented Hybrid ones, issue may be
resolved. Therefore this kind of place is needed with alternative source of power supply
which clean and everlasting one. As considering solar renewable energy is most prominent
one for this case. As area of Colombo is being mostly with high-rise buildings, roof-tops
are most suitable for installing photovoltaic panels.
Because, buildings that are able to self-sustain themselves and use solar energy that is
collected on-site are becoming an emerging trend. Net-zero energy buildings are buildings
that generate as much energy from renewable energy sources as they consume in an average
year [6].
1.2 Energy Demand Crisis and Favorable National Energy Policies
1.2.1 Experiencing Energy Demand Growth
During the first half of year 2015, maximum recorded electricity demand in Sri Lanka was
2210.4MW (excluding the contribution of SPP Mini Hydro, Solar and Biomass) which is a
higher value compared to the maximum demand of 2151.7MW in year 2014 [9].
With the increasing demand for energy to provide for the country’s economic and social
development, total primary energy demand is expected to increase to about 15,000 kTOE
by the year 2020 at an average annual growth rate of about 3%. Electricity and petroleum
sub-sectors are likely to record higher annual growth rates of about 7-8%. Hydro electricity
production and biomass-based energy supplies, which are the only large-scale indigenous
primary energy resources available in Sri Lanka, are expected to increase only marginally
in the near future. This is mainly due to limitations in further hydropower development
owing to lower economic viability of exploiting the remaining large hydropower sites and
limited use of biomass with gradually increasing standard of living of the population. This
means that the country’s incremental primary energy requirements need to be supplied
mainly by imported fossil fuels in the medium term. In the longer term, possible
development of indigenous petroleum resources and accelerated development of non-
conventional renewable energy are likely to make a significant change in Sri Lanka’s mix
of primary energy resources [10].

3
1.2.2 National Energy Policies
 Providing Basic Energy Needs
 Ensuring Energy Security
 Promoting Indigenous Resources
 Enhancing Energy Sector Management Capacity
 Consumer Protection and Ensuring a Level Playing Field
 Protection from Adverse Environmental Impacts Arising through Development
and Operation of Energy Facilities
With the support of National Energy Policies (i.e. Promoting Indigenous Resources) an
alternative way have to be found out as soon. Available systems wanted to be empowered
with Geospatial disciplines. Currently in Sri Lanka, The Ministry of Power and Renewable
Energy under Minister Hon. Ranjith Siyambalapitiya, is taking over so many favors to
uplifting and maintaining renewable energy power generation through new ideas. For a
good example,
Government also takes keen interest to uplifting and discovering new methods for enriching
power generation over renewable energy sources. The Ministry of Power and Renewable
Energy is responsible for this. For example project, such as “Soorya Bala Sangramaya” is
taking over by government. And even more under budget proposals of 2017, for the
recognition of the program “Battle for Solar energy” conduct by the ministry of power and
renewable energy. Their intention of this program is to establish one million solar power
stations on roofs. To motivate the public to join this program, they expected to give a
concession to the interest rate to the loans granted to purchase solar panels. Accordingly,
Rs.1500 Million has been allocated from the budget for that purpose. In addition, the budget
has proposed to fix solar panels on government buildings and Rs.350 Million had been
allocated for that as an initiative.
1.3 Problem Statement
However, whatever actions had been taken so far, there are lack of satisfactory result and
outcomes. This could be predicted by means of getting into this as in depth. The thing is no
matter how much of funds are going to be spent, but there is no appropriate well organized
techniques were utilized. As considering Colombo, when trying to utilizing roof-tops of
high-rise buildings and ordinary buildings, shadow impact of building which besides to
each one is should be taken into account. Without recognizing this effect, gain cannot be

4
achieved. As Sri Lanka being besides to equator, a lot of solar energy could be harvested.
In fact no need to consider seasonal changes also.
1.4 Research Objective
1.4.1 Major Objective
Major objective of this study is to determine possible potential rooftops for installing photo
voltaic solar panels in low land urban areas using spatial data modeling and analysis
techniques.
1.4.2 Minor Objective
To achieve the main objective, two minor objectives are defined,
 To identifying and excluding shadow casted rooftop areas
 To determining highly potential solar radiation hotspots
1.5 Scope and Limitation
1.5.1. Scope
Solar potential map creates to identifying most suitable hotspots for placement of
photovoltaic panels.
1.5.2 Limitation
The study mainly used 3D building models generated with respect to arbitrary heights due
to unavailability of satellite stereo pair images, aerial photos and DSM covering Colombo
municipal council area. So the obtained results doesn’t show the reality.

5
Chapter 2
2.0 THEORETICAL BACKROUND
This chapter commonly and particularly describes the research literature reviews and
definitions and conceptions of solar potential rooftops studies, its treatment process and
suitability criteria and their analysis for the site selection. Furthermore, it includes the
illustration of applied techniques, theoretical and practical concepts for this research, which
specifically describes shadow exclusion process and GIS analysis for suitability process.
2.1 Literature review
Most of the countries especially Asian countries have been launched different projects to
utilizing solar power in systematically and technically managed way for efficient outcome.
In fact, they are trying to integrating generated power into national power grid. For this
purpose Geospatial, technology addresses in a great level for enriching results actually.
These projects are being environmental friendly and help to conserving earth resources.
Several examples are given below to explain the use of Geospatial Sciences to convert solar
energy into electric power.
Since the advancement of PV technology buildings’ rooftops have become suitable regions
for generating electricity from the received solar irradiance (i.e., radiation incident on a
surface). Of course, not all rooftops are appropriate for the installation of PV systems due
to unsuitable topography and shadowing from the surrounding environment. Determination
of a rooftop’s suitability can be done using a manual survey by experts in this field.
However, this process is highly unsuitable for the estimation of Solar Potential (SP) over a
larger region with thousands of rooftops. A technically influenced approach is necessary
for larger regions [6].
2.2 Factor influencing in site selection
Selection of study area is due to its high suitability and least topographic variability [11].
Site selection process were done with considering following two criteria,

6
 Environmental aspect
 Social and Cultural Aspects
2.2.1 Environmental aspect
Factors, which influence the environmental feasibility of a land application site, wanted to
be addressed. Factors such as Land use of the area and Amount of Sun exposure.
2.2.1.1 Land use type
The decree contains a clear orientation to support the development and the dissemination
of PV systems built on the infrastructure coverage, while there are not additional incentives
for those to be installed on the ground, in order to avoid soil consumption, especially in
rural land, which could have implications for the food industry, strategic in the near future.
In this context it assumes considerable importance for the PV, the characterization of the
available surface on the roofs of residential and industrial buildings, including those of
urban settlements, where this type of "land use" is densely represented [7].
Usually solar power plant consuming a huge area of land. This could be a cause to
unavailability of vegetation lands. In every country, vegetation is being key role of national
economy. By acquiring large amount of land will lead to demolishing productivity of
nation. Therefore, another fair solution was figured out for selecting prominent sites.
Utilizing building rooftops could be considered as finest way. Most of the building rooftops
are being not deployed with specific purposes.
2.2.1.2 Amount of sun light exposure and seasonal changes over year
As Sri Lanka being besides to equator and situated in low latitude, usually the country
experiences higher amount of sun exposure over year. Even more, as being coastal areas
are most suitable spots for sites selection.
The amount of solar radiation reaching the surface depends on location, atmospheric
effects, and topography. Solar radiation is affected by the earth’s geometric rotation and
revolution around the sun [12].
It also varies with environmental factors like atmospheric attenuation effects including
cloud cover and water vapor [13]. On the ground, topographic effects such as elevation,
slope, and orientation influence the amount of radiation reaching a surface [12].

7
Sri Lanka is one of the few countries with long days and plenty of sunshine, intensity of
solar energy increases from north to south [14].
2.2.2 Social and cultural aspects
Social and Cultural Aspect, which are described in the following subsections, influence the
acceptability of a land application site. In this research considered Building, Energy needs
and Population Density.
2.2.2.1 Building
Buildings that are able to self-sustain themselves and use solar energy that is collected on-
site are becoming an emerging trend. Net-zero energy buildings are buildings that
generate as much energy from renewable energy sources as they consume in an average
year [1].
As urban areas being mostly with commercial buildings and residential, an assumption
could be made as most of land areas are covered by buildings. As utilizing building rooftops
are approximately equal to using whole land cover of specific area.
2.2.2.2 Energy shortage crisis and in seek of alternative way of source
In the day of energy crisis, fossil fuels becoming scare day by day due to limited reserves.
Hence, the time has to come to depend on renewable energy, which is called as “Clean
Energy”, “Green Energy” as well as “Alternative Energy”. Before the invention of fire, the
Sun was only source of energy on the earth surface. Solar energy is the most readily
available source of energy, which is free, as well as pollution frees [14].
Providing sufficient energy to meet the needs of urban dwellers is undoubtedly a
challenging task [2]. with excessive growth of population, transportation and industrial
zones, energy needs experience in higher demands. Especially in urban areas, this happens
in most probable manner.
2.2.2.3 Environmental friendly
Solar photovoltaic (PV) energy offers a sustainable way of providing society with a
renewable source of energy and can help decrease the reliance on fossil fuel consumption
[1].
The exhaustion of fossil fuel resources on a worldwide base has necessitated a crucial quest
for alternative energy sources to meet up the contemporary day demands. Solar energy is

8
clean, boundless, environment friendly and a potential resource among the various
renewable energy options [5].
2.3 Significant usage of 3D models in GIS
While we make comparison between 2D and 3D, 2D conveys one scale and one
representation per view while 3D does multiple scales and multiple representations per
view. This context tells us 3D is more precise than 2D. 3D GIS supports and enhances
planning, designing operations. As considering solar potential mapping, 3D building
models help to enhancing sort outs of right spots of solar panels installation. As study area
is being urban area
3D geographic information systems (GIS) modeling approach at a fine spatiotemporal
resolution to assess solar potential for the development of smart net-zero energy
communities [1].
2.3.1 3D building models
Geospatial professionals often rely on drawings and models of their projects to aid in their
work. However, 2D computer-aided design models do not always provide these
professionals with speed and accuracy. Therefore, they needed to find out solutions to make
changes. Architects around the world have used 3D modeling for many years to improve
the efficiency and aesthetic of their design. Major benefits of 3D modeling could be derived
as, speed, precision and control, scenario visualization and reduced lead-time.
2.3.2 Criteria to be considered in 3D building model
In fact, not all rooftops are appropriate for the installation of Photo Voltaic systems due to
shadowing from the surrounding environment (i.e. surrounded building and obstacles).
Determination of a rooftop’s suitability can be done using a manual survey by experts in
this field. However, this process is highly unsuitable for the estimation of Solar Potential
over a larger region with thousands of rooftops.
2.3.2.1 Utilization of accurate height
As regarding to solar potential studies, accurate heights having leading role on shadow
impact owing to daily and seasonal shifts of the sun, and effects of surrounding topography
and buildings [8]. Therefore building heights needed to be maintain well detailed and
accurate.

9
DEM is an array consisting of elevations. But DEM is not accurate for the study area
because DEM was older than buildings in study area as study concern was solar energy
potential from the rooftops rather than the flat surface. Up to date and high resolution DEM,
we demarcated 29,861 points on Google earth for the acquisition of altitude values for each
point at each house rooftop in the study area. These points were than interpolated by using
Inverse Distance Weighting (IDW) technique using ArcGIS spatial analyst tool [11].
2.3.2.2 Accurate geo location
Accurate geo-location have to be well conditioned to ensuring orientation of model,
accurate azimuth, sun orientation and sun path of a specific day with respect to the model
and related obstacles such as shadows.
2.4 Estimation of solar potential
2.4.1 Existing techniques used to figured out
2.4.1.1 Constant-value methods
Constant-value methods of estimating rooftop availability are popular due to their ease of
use; they are not time- or resource-intensive, and they provides a useful starting point for
potential rooftop solar energy generation in a region. Many of the constant-value methods
Figure 1: Variation of shadow casts for actual and arbitrary height inputs
Figure 2: Variation shadow casts for geo-located and non-geo-located model

10
of rooftop-area estimation consider typical rooftop configurations and estimate a multiplier
that can be applied to an entire region. Most of these studies make rule-of-thumb
assumptions about the proportion of sloped versus flat roofs, the number of buildings with
desirable rooftop orientations, and the amount of space obstructed by building components
such as heating, ventilation, and air-conditioning (HVAC) systems and shadows. A
variation of the constant-value method involves estimating available rooftop space based
on the population density of a region [15].
2.4.1.2 Manual selection methods
Manually selecting rooftops from sources such as aerial photography represents a much
more refined albeit more time-intensive method of identifying suitable rooftop space than
constant value methods.
2.4.1.3 GIS-based methods
The majority of rooftop analyses use GIS-based methods for estimating the suitable space
for rooftop PV. The key distinction between these methods and the previously discussed
methods is that decisions about rooftop suitability are not made using predetermined
constant values or by manually selecting buildings. Instead, ideal values for rooftop
characteristics are input into a computer model, and the GIS software determines areas of
high suitability. This often results in a quicker, more objective, and more accurate method
for identifying rooftop availability. GIS-based methods use primarily 3-D models to
determine solar resource or shadow effects on buildings. The 3-D models are most often
generated from ortho-photography or light detection and ranging (LiDAR) data, and they
are combined with slope, orientation, and building structure data to estimate total solar
energy generation potential. As LiDAR data has become more widely available at higher
resolutions in recent years, this has become a much more desirable method for estimating
rooftop area [15].
In the last two decades, several empirical solar radiation models have been enhanced by the
use of geographic information systems tools. The faster processing capabilities associated
with GIS platforms allows for integration of sophisticated solar radiation models and
additional consideration of the effects of topography on incoming solar radiation [16].
GIS tools let the user examine the temporal and spatial variability of incident solar radiation
on a landscape level [16].

11
2.4.2 Variance of present study from existing ones
Most of studies and work processes, which based on solar potential_ are dependent on data
availability, data acquisition, time consumption and cost effects. Especially LiDAR
technology had been utilized in number of studies. But there are limitation under data
availability, cost effective and professionals need to interpret and process data. As
considering to Sri Lanka, LiDAR technology not available yet. But renewable energy
sources such as solar power have to be utilized under national power grid because of
excessive needs of energy.
This present study able to be contributed a standalone, less cost and low time-consuming
process.
2.4.3 Considerable factors on modeling and analyzing
2.4.3.1 Application used to design solar potential
 Sketch Up Pro 2015 developed by Trimble
 ArcGIS Desktop 10.1 developed by ESRI
 Google Earth Pro developed by Google Inc.
Three more powerful and capable application were used to explore solar potential analysis.
Specifically SketchUp Pro is most prominent tool for analyze Sun path, position and impact
of shadow casts over generated model. This having capability of importing desired any
location of earth terrain model. Geo located 3D models such as buildings could be
generated by giving respective elevation input. In addition, casting shadows able to be
simulated at any customized location, year, date and time.
Of course, not all rooftops are appropriate for the installation of PV systems due to
unsuitable topography and shadowing from the surrounding environment [6].
To study the effect of the sun on what model have built (or plan to build) in a specific
geographic location, there is an integral part which called customized shadow setting of the
design of any built object. If a sunroom was designed, we need to know that the sun is
actually going to hit it, no? So SketchUp can be used to show exactly how the sun will
affect our creation, at every time of day, on every day of the year. The basic thing to
understand about shadows in SketchUp is that, just like in real life, they have controlled by

12
changing the position of the sun. If SketchUp were another kind of program, a bunch of
information about azimuths and angles have to be typed, but luckily, it is not. Because the
sun moves in exactly the same way every year, just pick a date and time, and SketchUp
automatically displays the correct shadows by figuring out where the sun should be. Hooray
for math! [8].
Google Earth imagery helped to make base map for digitization of each building in the
study area. There are numerous years of imagery is available inside of Google Earth Pro
software at the best eye alt. Google Earth Pro have to be highlighted here for being available
along with powerful tools and free of cost. Any part of the earth sphere could be viewed in
modes of orthogonal and perspective. In addition, these abundantly available features leads
to get achieved great results.
The Solar Analyst tool in ArcGIS allows for the modeling, mapping and analysis of solar
insolation over a geographic area for specific time frames. It takes into consideration the
effects from the atmosphere, latitude, elevation, inclination of slope, orientation, daily and
seasonal shifts of the sun, and effects of shadows cast by surrounding topography and
buildings [1].
Model builder feature inside ArcGIS Desktop supports to build up step by step procedures
of work flow. If any procedures have to be altered, no need to collapse everything,
correcting specific step is enough.
2.4.3.2 Solar radiation data
To achieving reliable outputs from hour based shadow casted raster of respective day, GIS
weighted overlay analysis has to be carried out. For this purpose solar radiation parameters
can be utilized to assigning weights as hour basis. Considerable parameters are,
 Direct Normal Insolation(DNI)
Direct solar radiation is the radiation that comes directly from the sun, with minimal
attenuation by the Earth’s atmosphere or obstacles.
 Diffused Horizontal Insolation(DHI)
Diffuse solar radiation is that which is scattered, absorbed, and reflected within the
atmosphere, mostly by clouds, but also by particulate matter and gas molecules.

13
 Global Horizontal Insolation(GHI)
The direct and diffuse components together are referred to as total or global radiation
(Figure 3). Solar radiation is presented as global radiation, which is calculated as the sum
of direct and diffuse radiation for a point or an area. Direct and diffuse totals are added to
determine total global radiation in watt-hours per square meter (Wh/m2) [12].
Figure 3: GHI value dispersion over Island

14
Chapter 3
3.0 METHEDOLOGY
This chapter elaborates on study area characteristics and proceeded methodology to
reveal solar potentiality over building rooftops for PV panel installation.
3.1 Description of study area
A land parcel in Colombo pettah, Western Province, Sri Lanka which around 450×140m
area mostly comprised with buildings was taken into account under this research purpose.
Area is centered at 6°56'7.05"N, 79°51'6.92"E and measures 63000m2
. The city often
described as warm tropical environment and less topographical variability. Annual average
of temperature and rainfall are 27°
C-29°C and 382mm respectively.
3.2 Data
Shadow analysis over 3D building models require accurate building heights and geo
location. As sun path and sun location related to earth object with time is depending on
accurate geo-location and shadow simulation is depending on accurate building height.
Building footprint data was derived by manual digitizing using Google terrain layer which
imported in Sketch Up. Arbitrary heights were used to building heights, because of
unavailability of satellite stereo pair images, aerial photos and DSM covering Colombo
municipal council area. Annual solar data which obtained from U.S. Government Open
Figure 4: Study Area

15
Data (www.opendata.gov) was processed for weight assignment on spatial analysis. Even
more solar datasets (approximately 25GB) of whole world which comprising with GeoTIFF
and ASCII formats were received from SolarGIS. But unfortunately those data could not
be utilized as required more computer hardware configurations.
3.3 Research design
Figure 5: Research design

16
Methodology is comprised with two major phases. Preceding one is modelling of realistic
3D building with arbitrary heights. Following one is analyzing multiple 2D rooftop raster
model to extract shadow casted sectors with the support of GIS spatial analyst.
3.3.1 Generation of geo-located 3d building models
Accurate geo-location is being vital importance to ensuring shadow simulations over
building rooftops as actual. SketchUp software having feature of importing geo referenced
terrain layer (i.e. powered by Google Earth). Building foot print was overlaid over desired
study area. By manually input height attribute to geo located building foot print, 3D
geolocated building model was created. But push/pull tool which was embedded in
SketchUp was able to construct only flat roof models. According to real situation, a little
bit complex rooftop types such as two sided, four sided, etc. were constructed by looking
Google Earth.
3.3.2 Shadow casted rooftop raster models
Utilization of customized shadow tool on SketchUp software, real time shadow simulations
could be done over any desired location on earth at any time. Multiple shadow casted raster
models were generated to a certain date for multiple hours (i.e. 8AM to 4PM). Shadow
movement over rooftops with respect to time series were observed.
With SketchUp’s Shadows feature, model could be casted with a basic shadow or see how
the sun casts shadows on or around a geo-located model.
While casting real-world shadows, SketchUp’s calculations are based on the following:
1. The model’s latitude and longitude
2. The model’s cardinal orientation (north, south, east or west; see Adjusting the
Drawing Axes for details about how the drawing axes are aligned to the cardinal
directions)
3. The selected time zone
3.3.3 Process of solar data for weight assignment
As shadow movement varies with time, light intensity of sun exposure continuously
changes. Because of this, every rooftop raster model could not be treated in same manner.
For this purpose, GHI data for a year (hour basis data for 365 days) was processed with the
support of Microsoft Excel. Average GHI values were used to assign as weights to each
hour raster models. Even more shadow casted raster models were reclassified into 4 classes

17
based on respective DN values. For reclassification purpose reclassify tool inside of
ArcGIS 10.1 Desktop was used.
3.3.4 Spatial analysis over shadow casted raster models
Regarding to present study, manual analysis methods is not able to support much because
of time consuming and uncertainty. Intervention of GIS techniques such as spatial analysis
was more powerful and capable to find more. Hour basis multiple shadow casted raster
models were converted into grey scale images with Erdas Imagine 9.1 to reduce image
complexity. Then images were reclassified with respect to their DN value ranges. Each
models were classified into four classes. Pixel values of shadow casted and open to sun
exposure were comprised to lower class and higher class respectively. Pixels which in
between the range was happened owing to different orientation of roofs (i.e. two sided, four
sided, etc.). To analyze multiple overlaying models, weighted overlay analysis was
executed by developing model builder in ArcGIS 10.1. In case of assigning weights to
multiple raster layers which included with multiple classes, were assigned with processed
GHI values by hourly.

18
Chapter 4
4.0 RESULTS AND DISCUSSION
This chapter discusses results of modelling efforts which were described in the
methodology and GIS spatial analysis with the help of model builder as well. GIS spatial
analysis process was performed to identify relationship and patterns in derived raster
dataset, in order to extrapolate and make predictions for PV potential over the entire study
area.
4.1 Geo-located 3D building model
Geolocated 3D building model was essential for reliable shadow simulation over building
rooftops. Building foot prints were derived from manual digitizing over terrain model
which was imported to SketchUp software. Created building foot prints consist certain level
of uncertainty. This happened because of digitized foot prints were actually rooftops instead
of building bottoms and less resolution images as well.
The Shadows feature can give a general idea of how the sun and shadows will look at a
specific location. The time is not adjusted for daylight saving time. If the model is geo-
located in an area where time zone lines zigzag rather dramatically, the time zone may be
off by an hour or longer.
Figure 6: Geo-located 3D building model

19
4.2 Multiple type of rooftop model
According to the sun movement of a certain day, sun light exposure over rooftops is not
being constant all over the time. Changes happen frequently with respect to time and
shifting of sun angle. Even more while considering to multiple designs of rooftops such as
two sided, four sided, etc. (Figure 7). Sun light exposure over rooftops could be biased. For
an instance, if a two sided rooftop taken into account, one aspect of rooftop which oriented
to sun is mostly potential for solar radiation than other face of rooftop in morning session.
If evening session is considered, other side of rooftop is more potential than morning
session. Flat rooftops are exposed to solar radiation symmetrically with daily shifting of
sun.
Figure 7 Various type of rooftops
4.4 Shadow casted building models
Below shown 9 models were exported as raster format from SketchUp. These raw raster
models depicting shadow simulation patterns over rooftop with respect to time variation of
a day. But while considering to shadow patterns over a year, variance would not been much
(i.e. neglect able). Because, when considered light source (i.e. sun) is being so far from
objects, simulated shadow patterns over objects are not much differed from day by day
patterns. Sun is being about 149.6 million km far from earth. And as Sri Lanka is being low
latitude country, solar radiation is characterized as high availability and uniformity. While
take a precise look on (Figure 8 and 9), shadow patterns are simulating westward to
eastward with time. A sector which being higher potential for solar radiation in morning

20
will be turned to less potential in evening. Darker to lighter values are depicting shadow
casts to higher solar exposures. Values which are lying in between the range, are denoting
solar radiating variation over multiple type of rooftops. SketchUp software is enabled to
setup sun position and shifting angle with respect to customized time inputs and desired
geographical locations. Shadow simulation is a dynamic thing with time. Therefore, hour
basis multiple model have to be considered while analyzing solar potentiality. Eliminating
of shadow casted areas over rooftops would be a challenging issue unless the utilization of
geospatial techniques. Without using any software suchlike SketchUp, shadow patterns’
movement could not predicted or modelled as well.
Figure 8: Temporal shadow casting model over rooftop (Perspective view)
±
08:00 09:00 10:00
11:00 12:00 13:00
14:00 15:00 16:00

21
4.5 Reclassified shadow casted raster models
Considering to DN values of shadow casted rooftop raster models, four desired classes were
derived with the help of ArcGIS 10.1 Desktop to implementing GIS spatial analysis. To
obtaining solar potential rooftop map, created 9 temporal shadow pattern models had to be
defined that each model does how much influencing on final model. For this purpose,
±
0 0.1 0.2 0.3 0.4
0.05
Kilometers
08:00 09:00 10:00
11:00 12:00 13:00
14:00 15:00 16:00
Figure 9: Temporal shadow casting model over rooftop (Orthographic view)

22
orthographic view of shadow temporal models are well suitable than perspective view
(Figure 8 and 9). Each model and their respective classes had to be discriminated by giving
their respective weights.
Above depicted diagram is conveying (Figure 10), how do shadow patterns and intensity
values are changing over rooftops along with time variation. Diagram gives clear picture
that lower value “1” as shadow cover and higher value “7” as high solar exposure. Values
which in between (3 and 5) were owing to different types of rooftop and rooftop
08:00 09:00 10:00
11:00 12:00 13:00
14:00 15:00 16:00
0 0.1 0.2 0.3 0.4
0.05
Kilometers
±
Figure 10: Reclassified shadow casted raster models

23
orientations to sun. In fact, higher value classes in each model could not be considered in
same manner. For an example, if higher classes (value 1) of two models such as 8AM and
12AM were taken into account, intensities of both classes are not same because of varying
sunlight exposure with time. Higher class (value 1) intensity of 12AM is well known as
greater than 8AM. Analyzing this kind of stuff is not easier unless utilizing powerful tools
such as geospatial techniques. To differentiating each classes of generated 9 models were
made possible by integrating weighted overlay analysis by using GIS application.
4.6 Weighted overlay analysis
4.6.1 Weight assignment
Classified multiple raster models (9 models) which based on time series (0800h to 1600h
of a certain day) were analyzed by using GIS weighted overlay disciplines. As being
multiple models comprised with number of classes, weighted overlay analysis was most
preferable one. To assigning weights, solar data (i.e. GHI solar data which comprised of
DNI and DHI) was utilized with the support of weighted overlay sub tool which included
with spatial analyst tool of ArcGIS 10.1 Desktop. And especially model builder is needed
to be mentioned as been a powerful tool which made an independent environment while
doing analysis (Figure 11).
Hour basis annual solar data which comprised of 365 days was prosessed with Microsoft
Excel to determine weights. As earlier expalined, to figure out influence of each raster
model over final output, every layer was assigned with appropriate weights according to
their computed percentage values. The last row of the table below is depicting solar
radiation influence percentages of each hour basis model. Yearly solar data was averaged
to respective months (Figure 13). Afterwards values of each month were averaged. Finally
averaged values were determined to percentages of respective hours (See Table 1).
Weights of each hourly model were assigned by using final derived weights. But classes
which being inside of each model were assigned with partials of final derived weights.
While refering to processed solar data, computed weights of period before noon are being
greater than period of afternoon. Even looking at averaged GHI values also clarrifying same
thing (Figure 14 and 15). While assigning partial weights to each model's respective classes
(Figure 10), lowest value of each model (value 1) was assigned with "restricted". Because
value 1 was considered as shadow covered parts of every model. Meanwhile highest value

24
of each model "value 7" was assigned with greater values. Highest values which been
before noon were assigned with little bit greater values than afternoon. Weights of in
between values (value 3 and 5) were assigned with assumed values by refering to balance
amount of final derived weights after subtracted for highest value assignments (Figure 12).
`
MONTH 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00
JAN 217.419355 403.9677419 535.58065 635.12903 667.483871 622.54839 583.80645 419.12903 276.322581
FEB 227.857143 439.0714286 608.32143 717.32143 783.107143 737.03571 653.67857 512.25 333.928571
MAR 257.967742 456.7096774 616.3871 773.48387 817.225806 649.67742 573.90323 474.25806 301
APR 301.7 509.6666667 626.96667 746.7 721.966667 734.86667 628.53333 437.46667 223.1
MAY 300.774194 464.9032258 589.41935 670.80645 676.774194 551.32258 481.3871 409.16129 277.129032
JUN 268.333333 419.4333333 575.06667 663.33333 686.4 609.23333 471.96667 376.06667 235.133333
JUL 196.354839 341.3870968 465.93548 580.96774 578.580645 537.6129 416.12903 308.3871 232.612903
AUG 231 402.2258065 545.83871 639.29032 613.935484 459.74194 430.03226 358.6129 213.322581
SEP 310.866667 497.6 673.96667 709.9 667.933333 660.23333 518.4 353.7 197.166667
OCT 329.83871 526.9032258 678.54839 758.80645 797.709677 678.51613 524.90323 404.06452 203.129032
NOV 250.8 400.5 519.6 566.03333 554.766667 515.7 368.83333 187.13333 82.8666667
DEC 274.322581 475.6451613 638.6129 753.90323 784.193548 756.16129 662.87097 500.19355 287.709677 TOTAL GHI
AVERAGE GHI 263.936214 444.834447 589.52033 684.6396 695.839753 626.05414 526.20368 395.03526 238.61842 4464.681848
PERCENTAGE VALUE(%) 5.91164662 9.963407519 13.204084 15.334566 15.5854275 14.022368 11.785917 8.8480047 5.34457837
ROUNDED VALUE/WEIGHTS 5.9 10 13.2 15.3 15.6 14 11.8 8.8 5.3 TOTAL INFLUENCE
FINAL DERIVED WEIGHTS 6 10 13 15 16 14 12 9 5 100
HOUR BASIS AVERAGE GHI VALUE FOR EACH MONTH(Wh/m2)
Table 1: Solar data processing
Figure 11: Model builder of spatial analysis

25
Rater Influence(%) Field Scale Value
reclass_0800h 6 Value ↖
1 Restricted
3 1
5 1
7 4
1 Restricted
3 1
5 2
7 7
1 Restricted
3 2
5 3
7 8
1 Restricted
3 2
5 4
7 9
1 Restricted
3 2
5 4
7 10
1 Restricted
3 3
5 3
7 8
1 Restricted
3 2
5 3
7 7
1 Restricted
3 2
5 2
7 5
1 Restricted
3 1
5 1
7 3
100
Figure 12: Assigned weights in weighted overlay analysis

26
A rough idea on varying GHI values of respective hours with entire months could be
obtained.
By summarizing monthly GHI value of respective hours for a day,
0
100
200
300
400
500
600
700
800
900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
GHI
VALUE
(Wh/m2)
MONTH
MONTHLY HOUR BASIS SOLAR RADIATION
08:00 09:00 10:00 11:00 12:00
13:00 14:00 15:00 16:00
263.936214
444.834447
589.52033
684.6396 695.839753
626.05414
526.20368
395.03526
238.61842
0
100
200
300
400
500
600
700
800
08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00
GHI
VALUE
(Wh/m2)
TIME
HOUR BASIS SOLAR RADIATION OF A DAY
CHANGE OF SOLAR RADIATION
Figure 13: Monthly hour basis solar radiation
Figure 14: Hour basis solar radiation of a day

27
Weights which had to be assigned for final model called solar potential rooftop map were
derived from solar data processing,
4.7 Solar potential rooftop model
The final desired result of this study is solar potential rooftop map (Figure 16). By achieving
this user even an ordinary man or woman would be able to find out solar potential spot over
their rooftops.
But unfortunately unable to figure out any systematic strategies to assigning weights or
validating results. While trying to validate final results with real time environment,
predicting shadow patterns manually over rooftops was not possible without any
intervention of tools or software. And as considering to field surveying was not possible
because of time shortage and inconsistencies of working at site such as overcrowded such
Colombo with people and vehicles as well.
Unavailability of actual height data of buildings which needed to be maintained with great
level of attention, was unable to be worked out. But as our country being under
development and experiencing excessive energy needs, this kind of alternative method of
power generation sources such as solar power wanted to be developed well. Solution could
5.9
10
13.2
15.3 15.6
14
11.8
8.8
5.3
0
2
4
6
8
10
12
14
16
18
00:00 02:24 04:48 07:12 09:36 12:00 14:24 16:48
ASSIGNED
WEIGHTS
TIME SERIES
ASSIGNED WEIGHTS
Figure 15: Derived weights

28
be made by maintaining Building Information Modelling (BIM) in great level with up to
date contents.
Most of parts looks solar as potentially good. Meanwhile as considering to sector in
zoomed view which including different level of suitability (Figure 17)
Figure 16: Solar potential rooftop map

29
The output of weighted overlay analysis was solar potential rooftop model. This was
included with multiple values. Values are denoting suitability of least to greatest. To
deriving final model, values were reordered and depicted. The chronological order is,
0 – Not Suitable
1 – Less Suitable
3 – Moderate Suitable
5 – High Suitable
Not suitable (Value 0) is the sector which covered by shadow effect and High suitable
(Value 5) is denoting fully open area which was not obstructed by things. Values in-
between such (Value 1 and 3) was owing to different orientation of roof types and sunlight
exposures.
5
0
3
1
Figure 17: Zoomed view of solar potential rooftop map

30
Chapter 5
5.0 CONCLUSION AND RECOMMENDATION
This chapter conveys fulfillment of derived results and suggested recommendations for
future works. Not only strengths but also shortcoming which had to be faced while
processes were concisely clarified.
5.1 Conclusion
The present study has reached to satisfactory level of achievement. Defining solar potential
rooftop sectors by using geospatial techniques is robust and low time consuming than
manual methods. In fact, while trying to find out solar potential hotspots over high rise
buildings comprised area is little bit complex. This difficulty was caused due to shadow
impact by buildings which besides to each other. But these were simplified by geospatial
techniques and 3D modelling. Especially SketchUp Pro application have to be mentioned
as pillar of this study. Movement of sun over required area and shadow simulations were
figured out in reliable manner. In addition, spatial analysis techniques were supported well
for analyzing temporal changes of shadow impact and solar radiation. Solar potential map
was very desirable one for find out feasibility of solar radiation availability over rooftops.
Reliable shadow simulation is depend on accurate building heights. Unfortunately, as
unavailability of height sources such like stereo pair images, aerial photos and DSM for
study area actual results could not be achieved. But, this method is most prominent to any
complex area with buildings
5.2 Fulfilment of proposed study
Final results which accorded with objectives of study, had been reached to satisfactory
level. Impact of shadow potential over rooftops were resolved and suitable spots were
found out by excluding less suitable. By providing geo-rectified map or diagram, even
ordinary people are able to be find out right spot for their residents.
Reducing the amount of GHGs’ emissions is not directly connected to work process. But
by extending this process in wider extent, productivity of power could be utilized to replace
the usage of petroleum mode of transportation. As exploring previous context in detail,

31
mostly proposed study area is enriched with vehicular movements. This causes excessive
emissions of GHGs mostly concentrated of CO2. Excessive existence of carbon dioxide
keeps city warm. By transforming to solar power oriented electrical generation which could
be used to electrical vehicles, makes environment as green.
5.3 Future research
5.3.1 Implementation of geo-portal hubs in solar potential
Generally web mapping facilitates to ease of access, maintains to being up to date, and
experiencing dynamic environment. Utilization of web mapping of solar potential would
be a great innovation. While solar potential study concludes with web mapping, tremendous
benefits could be archived. The desired output would be outstanding one. People always
looking for simplify and build on what they’re doing. Introducing solar potential discipline
by means of geoportal, is having capability of developing and maintaining with respect to
real time environment.
Geo hub portal enables us to reinvent the way we are delivering services and broadens our
ability to engage everyday way. Geo hub dynamically integrates real time data onto this
user focused map [17].
For an instance, while newly buildings are constructed between existing ones, undefined
impacts would be introduced. These kind of issues could be tackled by integrating multiple
entities such as data providers, developers, analyst, etc. Geo-portals support to ease of
accessibility. Even public enable to assess the availability of sun exposure over their own
residents to make solar potential with precise locations.
5.3.2 Establishment of solar electric filling stations
Solar electric filling stations, could be developed with respect to established solar potential
spots and assessing GIS network analysis. This facilitates and supports to electrical vehicle
consumption. Extensive benefits would be experienced as environment could be sustained
to greener and transportation made into smarter. Upcoming technologies such as wireless
charging, long life batteries and economic power consuming motors are led to being
everlasting technology.

32
5.4 Recommendation
Accurate building heights have to be utilized for realistic results. In present study multiple
roof types were constructed manually. It was time consuming and difficult. But while using
DSM of desired study area which derived from LiDAR is most prominent way to generate
complex type of roofs. Even more accurate building heights also could be obtainable.
Integration of LiDAR data processing into this study is strongly required.
Another important aspect have to be considered that data which used to modelling and
analysis have to be maintained up to date. For an example if new constructions arrived to
respective area, existing models are not able to convey reliable results.
Figure 18: Solar electric power filling station

33
6.0 REFERENCES
1. Annie Chow, Alan S. Fung,Songnian. (2014) GIS Modeling of Solar Neighborhood
Potential at a FineSpatiotemporal Resolution. Open access journal on building
science, v. 4, pp.195-206.
2. Nada M. Mohammed Kadhim, Monjur Mourshed, Michaela Bray. (2015)
Automatic Extraction of Urban Structures based on Shadow information from
Satellite imagery. 14th Conference of International Building Performance
Simulation Association, Hyderabad, India, Dec. 7-9, 2015
3. Samira SAADI, Mahmoud BENSAIBI. (2014) DETECTION OF BUILDINGS
HEIGHT USING SATELLITEMONOSCOPIC IMAGE. Second European
conference on Earthquake Engineering and Seismology, Istanbul Aug 25-29, 2014
4. N.D.Mani, Penmaya Ningshen. (2016) USING GIS AND REMOTE SENSING
TECHNIQUESFOR SOLAR HOTSPOTS INSTALLATION IN THIRUNAGAR,
DINDIGUL DISTRICT. Mechanical Engineering: An International Journal (
MEIJ), Vol. 3, No. 1, February 2016
5. Deepak Kumar, Sulochana Shekhar. (2014) COMPUTING BUILDING ROOFTOP
SOLAR POTENTIAL FOR PHOTOVOLTAICS. ISPRS TC VIII International
Symposium on “Operational Remote Sensing Applications: Opportunities, Progress
and Challenges”,Hyderabad, India, December 9 – 12, 2014
6. Matej Brumen, Niko Lukac, Borutˇ Zalik. (2014) GIS Application for Solar
Potential Estimation on Buildings Roofs. The Second International Conference on
Building and Exploring Web Based Environments
7. Flavio Borfecchia, Maurizio Pollino, Luigi De Cecco, Sandro Martini, Luigi
LaPorta, Alessandro Marucci, Emanuela Caiaffa. (2013) Integrated GIS and
Remote Sensing techniques tosupport PV potential assessment of roofs in urban
areas. International Conference on Computational Science and Its Applications,
ICCSA 2013 pp. 422-437
8. Aidan Chopra. (2007) Google SketchUp for Dummies. Wiley Publishing, Inc.,
Indianapolis, Indiana
9. Public Utilities Commission of Sri Lanka. (2015) Generation Performance in Sri
Lanka 2015 (First Half), p1

34
10. National Energy Policy & Strategies of Sri Lanka, Ministry Of Power and Energy.
Government of Sri Lanka. (2008). The Gazette of the Democratic Socialist Republic
of Sri Lanka (PART I: SECTION (I), No. 1553/10), p5A
11. Urooj Saeed, Muhammad Luqman, Samiullah Khan, Imtiyaz Younas. (2016)
Optical Remote Sensing and GIS Techniques for Evaluation of the Potential of
Solar Photovoltaic Panels for reducing GHGs and Urban Heat Islands. 1st National
Conference on Environmental Engineering & Management
12. Caroline Carl. (2014) CALCULATING SOLAR PHOTOVOLTAIC POTENTIAL
ON RESIDENTIAL ROOFTOPS IN KAILUA KONA, HAWAII. Unpublished
master’s thesis, FACULTY OF THE USC GRADUATE SCHOOL, UNIVERSITY
OF SOUTHERN CALIFORNIA
13. Fu and Rich. (1999) DESIGN AND IMPLEMENTATION OF THE SOLAR
ANALYST: AN ARCVIEW EXTENTION FOR MODELLING SOLAR
RADIATION AT LANDSCAPE SCALES. Proceeding of Nineteenth Annual ESRI
User Conference.
14. Sardar A Patil, Sonal Khobrgade, Shweta Khandelwal, Harpreet Singh, Asfa
Siddiqui. (2015) An estimation of solar energy potential using point solar radiation
tool in ARC-GIS: A case study of College of Engineering, Pune International
Journal of Applied Research 2015; 1(9): pp890-897
15. J. Melius, R. Margolis, S. Ong. (2013) Estimating Rooftop Suitability for PV: A
Review of Methods, Patents, and Validation Techniques. NREL is a national
laboratory of the U.S. Department of Energy, Office of Energy Efficiency &
Renewable Energy Operated by the Alliance for Sustainable Energy, LLC
16. Dongrong Li. (2013) Using GIS and Remote Sensing Techniques for Solar Panel
Installation Site Selection. Unpublished master’s thesis, University of Waterloo
17. LILIAN P.CORAL, MPP (2016, JUL 16). CITY OF LOS ANGELES GEO HUB.
Retrieved from https://www.youtube.com/watch?v=etdwa768Zms. Accessed on
2017.03.20

ASSESMENT OF SOLAR POTENTIAL ROOFTOPS USING GIS AND REMOTE SENSING TECHNIQUES

Recommandé

Recommandé

Contenu connexe

Similaire à ASSESMENT OF SOLAR POTENTIAL ROOFTOPS USING GIS AND REMOTE SENSING TECHNIQUES

Similaire à ASSESMENT OF SOLAR POTENTIAL ROOFTOPS USING GIS AND REMOTE SENSING TECHNIQUES (20)

Plus de Jessica Henderson

Plus de Jessica Henderson (20)

Dernier

Dernier (20)

ASSESMENT OF SOLAR POTENTIAL ROOFTOPS USING GIS AND REMOTE SENSING TECHNIQUES