University of Southern California Dissertations and Theses

Modeling the spatio-temporal variability of solar radiation on buildings: a case study of Lewis Hall

(USC Thesis Other)

Modeling the spatio-temporal variability of solar radiation on buildings: a case study of Lewis Hall

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

MODELING THE SPATIO-TEMPORAL VARIABILITY OF SOLAR RADIATION
ON BUILDINGS:
A CASE STUDY OF LEWIS HALL

by
Amanda Kim Laur

A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMTION SCIENCE AND TECHNOLOGY)

May 2014

Copyright 2014 Amanda Kim Laur

ii

ACKNOWLEDGMENTS
I want to thank Dr. John Wilson for his continuous guidance, expert advice, and patience
throughout all stages of this thesis. I would also like to thank Dr. Burcin Becerik-Gerber
for allowing me access to some of her research products, which provided a foundation for
my thesis. Finally, I would like to thank Dr. Darren Ruddell for serving on my thesis
committee and Dr. Robert Vos for helping me shape my thesis project

iii

TABLE OF CONTENTS
Acknowledgments ii
List of Tables v
List of Figures vi
List of Abbreviations viii
Abstract x
Chapter One: Introduction 1
1.1 Research objective 2
1.2 Purpose 4
1.3 Motivation 4
1.4 Organization of the Thesis 6
Chapter Two: Background and Related Work 7
2.1 Building Energy and Efficiency Background 7
2.1.1 U.S. Energy Consumption 8
2.1.2 HVAC Background 9
2.2 Building Energy Conservation Strategies 11
2.2.1 Demand Driven HVAC 12
2.2.2 USC BLEMS Project 15
2.3 Solar Radiation 18
2.3.1 Global Factors 20
2.3.2 Landscape Facors 23
2.3.3 Atmospheric Factors 24
2.4 Solar Radiation and HeatTransfer Effects in Buildings 25
2.4.1 Case study: Urban Density and Passive Solar Heat Gains 27
2.5 Solar Modeling and Building Simulation Tools 28
2.5.1 Calculation Based Models 28
2.5.2 Two-dimensional Modeling 30
2.5.3 Three- dimensional Modeling 33

Chapter Three: Methods And Data Sources 36
3.1 Study Area 36
3.2 Workflow 39
3.2.1 Data Processing 39
3.2.2 Solar analysis Input Stage 42
3.2.3 Phases of Analysis 46

iv

Chapter Four: Results 48
4.1 Phase 01 Results 50
4.2 Phase 02 Results 55
4.3 Phase 03 Results 59
Chapter Five: Discussion and Conclusion 64
5.1 Implementation and Integration 66
5.2 Solar radiation model Valaidation 66
5.3 Study Limitations 67
5.4 Opportunties for Future Research 68

References 71
Appendix A: Charts and Floor Plans 74
Appendix B: Graphic Results 79
Appendix C: Result Charts by BLEMS Zone 92
Appendix D: Imapct Charts by BLEMS Zone 97

v

LIST OF TABLES
Table 1: Building energy management strategies 13
Table 2: Summary of software characteristics used for solar modeling
and building energy analysis 29
Table 3: Input values for Ecotect solar access analysis tool 43
Table 4: Summary of results by BLEMS zone 53

vi

LIST OF FIGURES
Figure 1: World and U.S. energy consumption
Source: Data adapted from Kelso (2011) 8
Figure 2: Building energy usage by percentage
Source: Data adapted from Kelso (2011) 9
Figure 3: BLEMS Hierarchy (from Rossler 2012) 15
Figure 4: Components of solar radiation
Source: Data adapted from Campbell and Wayne (2011) 19
Figure 5: Annual elliptical path of the Earth around the Sun
(from NOAA 2013) 21
Figure 6: Solar altitude over a year based on Los Angeles, CA 22
Figure 7: Lewis Hall sketch (North façade)
Source: Data obtained from USC Facilities Management
adapted using Autodesk REVIT (2011) 37
Figure 8: USC Campus Site Map
Source: Data obtained from USC Facilities Management
adapted using Esri ArcMap10 38
Figure 9: Data processing flow chart 40
Figure 10: Sky dome in 5° by 5° subdivisions (from Marsh 2007) 45
Figure 11: Example shading masks in stereographic view (left) and three
dimensional views (right) (Ecotect 2011) 45
Figure 12: Progression of Phases: Phase01 (top), Phase02 (middle),
Phase03 (bottom) 46
Figure 13: Building orientation (28° East of North) 49
Figure 14: Phase 01 results by orientation 51
Figure 15: Phase 01 results by floor 52
Figure 16: Phase 02 results by orientation 56

vii

Figure 17: Phase 02 impacts 57
Figure 18: Phase 02 results by floor 58
Figure 19: Phase 03 results by orientation 60
Figure 20: Phase 03 impacts 61
Figure 21: Phase 03 results by floor 61
Figure 22: Variations in roof solar radiation predictions over the three phases 63
Figure 23: Sunpath chart for Los Angeles, CA (UO 2007) 74
Figure 24: Lewis hall Floor Plan with BLEMS zones (1
st
Floor) 75
Figure 25: Lewis hall Floor Plan with BLEMS zones (2
nd
Floor) 76
Figure 26: Lewis hall Floor Plan with BLEMS zones (3
rd
Floor) 77
Figure 27: Three dimensional view of building envelope with orientations 78
Figure 28: Phase 01 results 79-82
Figure 29: Phase 02 results 83-86
Figure 30: Phase 03 results 87-90
Figure 31: Annual comparisons of all three phases 91
Figure 32: Comparison of phases by BLEMS zones 92-96
Figure 33: Comparison of total impact by BLEMS zone 97-101

viii

LIST OF ABBREVIATIONS
AHU Air Handler Unit
ASHRAE American Society of Heating, Refrigerating and Air-Conditioning
Engineers
ANSI American National Standards Institute
BIM Building Information Modeling
BLEMS Building Level Energy Management System
BMS Building management System
CAD Computer Aided Drafting
DOE Department of Energy
DEM Digital Elevation Model
DTM Digital Terrain Model
ESRA European Solar Radiation Atlas
FORTRAN FORmula TRANsaltion
GIS Geographic Information System
GRASS Geographical Resource Analysis Support System
HVAC Heating, Ventilation and Air-Conditioning
PIR Passive Infrared (sensor)
RGL Ralph and Goldy Lewis Hall
SEDS State Energy Data System
SQL Status Query Language
UO University of Oregon

ix

USC University of Southern California
VAV Variable Air Volume (Box)

x

ABSTRACT
The Sun is the center of our galaxy and its patterns have been studied by civilizations
since the beginning of time. Solar energy is a complex phenomenon that is the basis for
life on Earth. Understanding the position of the Sun during the day is critical for
evaluating how its energy impacts our daily lives. In an urban environment, the Sun’s
energy can be considered as a service as well as a burden. Solar energy is beneficial when
it can be harnessed using solar collectors for electric generation or when it contributes to
heat energy with passive heat gains in the winter. However, solar energy can cause
unwanted heat gains during warm summer months when buildings are trying to keep
occupants cool. Solar radiation models used to evaluate favorable conditions and
locations have traditionally only required two-dimensional data for evaluation of terrain
and rooftops. However, in order to attempt a comprehensive assessment of solar radiation
effects with a built environment, three-dimensional data must be used to evaluate vertical
surfaces as well. The proposed research can be used to evaluate solar radiation variations
at a temporal scale resulting from a building’s location as well as spatial variations
resulting from changes in the urban landscape. The investigation is centered on an
educational building, Lewis Hall, located on the University Park campus of the
University of Southern California. The impacts of solar energy evident in the following
research should be considered when evaluating and designing efficient building energy
systems in the future.

1

CHAPTER ONE: INTRODUCTION
Building energy efficiency is the first step toward achieving sustainability in building
operations. Energy efficiency helps control rising energy costs, reduces environmental
footprints, and increases the value and competitiveness of buildings. A building’s
location has a major impact on the amount of energy consumption required to keep its
occupants comfortable. Depending on geographic location, sun angles, wind speed and
wind direction are just a few of the climatic factors that affect building performance. This
leaves the question to be asked: how do a building’s location, geometry and orientation
affect a building’s interior conditions?
This thesis project focuses on how solar energy impacts a building’s envelope
based on its geographic coordinates. The study aimed to provide a basis for determining
what effects a building’s location has on its energy efficiency performance and will
demonstrate the need to link spatially-explicit environmental information with building
control systems. This information may facilitate advanced programming which could
allow prediction models to adjust for various conditions and to optimize building
performance as well as identify specific areas of a building that may need improvements.
Current building energy management systems anticipate the dynamic environment of the
interior of a building, but what about the continuously changing environment surrounding
a building? Building orientation and site considerations are typically evaluated in the
early design phases of a building, but can these factors be evaluated at a more intricate
level after the building has been occupied?

2

The envelope of a building is not only a two-dimensional external surface; it is
also a three-dimensional object, a space where connections between outdoor forces and
indoor conditions occur influenced by building materials and geometries. The envelope
also has a fourth dimension, it changes with time and season, which, in turn, has a
noticeable effect on the façade. Solar energy is either absorbed or transmitted by these
materials while daylight is admitted or rejected. Building efficiency is closely connected
to climate as well as the sun’s energy. HVAC systems are either battling against the
Sun’s heat trying to keep the occupants cool, or making up for its absence by warming
the occupants. Solar energy will be the focus of this research and the motivating research
question is as follows: How does solar energy impact a building’s envelope and how does
this vary over the course of the year and with building and landscape changes?
1.1 Research Objective
The guiding principle for this thesis is that improved building operation is feasible with
respect to energy management and indoor environmental quality using GIS technology.
The main objective is to demonstrate that outdoor environmental factors directly impact a
building’s internal environment through space and time. Although these effects are
dynamic, they are also predictable, and may be used to program improved building
control systems.
The Sun’s energy has the most significant impact on the building exterior
compared to other external factors, such as weather patterns. The patterns of the Sun’s
path, angles, and intensity are also very predictable. For these reasons, the main focus

3

will be on solar energy impacts on the buildings themselves and their correlation to
building performance.
Building orientation and exposures are usually considered in the beginning phases
of building design, i.e. the southern exposure is acknowledged to receive the largest about
of solar heat and light energy from the Sun in the northern hemisphere for example.
However, these factors are not typically evaluated in detail when documenting an
existing building’s energy efficiency.
The research study is centered on the Ralph and Goldy Lewis Hall, which is
located on the University Park campus of the University of Southern California, in Los
Angeles, California The amount of solar energy reaching the building’s envelope is
dependent on the urban landscape and/or surrounding built environment. In an urban
setting, landscape transformations are common yet predictable. Therefore, current
changes in landscape and site context surrounding Lewis hall will be taken into account
in three progressive phases as part of a GIS-inspired solar impact analysis. The key
questions to this study are:
1. What areas of the building receive the most solar radiation considering its
true orientation and position and how does this change throughout the
year?
2. How large is the impact from surrounding landscape features on the total
solar energy input compared to unobstructed access?
3. How large is the impact from existing and new buildings on the total solar
energy input compared to an unobstructed view?

4

1.2 Purpose
The cost of building operations far exceeds the cost of building construction (Muneer
2004). A sustainable building uses intelligent systems which respond to conditions in
real-time while maintaining the comfort of the occupants (Yang et al. 2011). The main
focus of recent research about designing efficient building energy systems acknowledges
the dynamic behavior of the building occupants through predictable patterns, class
scheduling, and real-time data from sensors. The thermal comfort of a building’s
occupants is the ultimate goal of HVAC systems. Yet, the existence of HVAC systems is
to compensate for the exterior conditions by raising or lowering temperatures to an
acceptable pre-determined level. Coupling exterior conditions with internal occupancy
information will provide more detailed information to smart HVAC computer systems. In
addition, an examination of the solar impacts on an existing building may provide insight
into permanent or temporary changes that may be considered, to the building’s design
that may, in turn, lessen the load on HVAC systems. The results may provide a
framework to make further adjustments to existing building energy management systems,
resulting in higher efficiency and greater understanding of external effects on a building
envelope.
1.3 Motivation
The problems associated with rapid urbanization of the world and its future sustainability
cannot be solved without new technologies (Maktav, Erbek, and Jurgens 2005).
Traditional methods for designing buildings and their associated energy systems do not
fully take into account the spatial aspects of a building’s location. However, the

5

capabilities of Geographic Information Systems (GIS) technology coupled with Building
Information Modeling (BIM) and energy simulation modeling provide new and
innovative ways of analyzing how buildings function within their environment.
According to the U.S. Department of Energy (DOE), buildings consume 70% of
the electricity used in the U.S. (Kelso 2011). Therefore, small improvements in building
performance can have a substantial impact on the current energy crisis. Buildings and
their associated systems are designed and programmed to suit the needs of the majority of
their occupants. However, using broad standards, many assumptions are made in the
design process. Consequently, many of the occupants may not be satisfied with the
building’s performance. Incorporating geospatial information throughout this process
means that design performance can be evaluated using actual local conditions providing
potential for more precise control and meeting occupant’s needs.
The overarching motivation for this thesis research project was to demonstrate
how spatial data could be incorporated into current systems and protocols such as the
behavior-based BLEMS study being conducted on Lewis Hall. The thesis documents how
the Sun’s patterns affect a specific building within the changing urban landscape.
Contrary to traditional GIS solar studies, this evaluation will use a three-dimensional
simulation model, to analyze all four exposures of the building’s envelope. In addition,
there will be a discussion and evaluation of how this new information may improve
HVAC systems by incorporating spatially and temporally dynamic environmental
attributes into energy simulation models. The final result of the thesis research will be a
more comprehensive understanding of how solar energy impacts building performance at

6

a thermal zone level, which may lead to future improvements in building energy
management systems.
1.4 Organization of the Thesis
This remainder of this thesis is arranged as follows. Chapter two summarizes prior
research and methods for conserving building energy. This chapter also includes an
overview of solar energy fundamentals and concludes with a summary of software
programs that were evaluated according to the needs of this study. Chapter three starts by
describing the case study building and surrounding geographic area. This is followed by a
discussion of the methods and data that were used to conduct a comprehensive solar
analysis. An overview of software inputs and explanations of the different levels of
analysis are also provided in this chapter. Chapter four presents and discusses the results
of the solar analysis study, highlighting what happens to solar energy inputs as you add
existing buildings, new buildings, and various landscaping elements. Chapter five
discusses the broader significance of the results, presents the conclusions that can be
drawn from the analyses in addition to addressing the validation of the methods used, and
briefly offers some suggestions for future work.

7

CHAPTER TWO: BACKGROUND AND RELATED WORK
There is limited research combining the fields of spatial science and building information
management. For realistic evaluations, site-specific factors need to be included within
whole building energy simulations to accurately assess their influence on building
operation. Current energy management systems tend to focus on internal factors to
evaluate and improve building efficiency. For example, several studies that have been
conducted evaluated how occupancy alone can be used to improve building operations.
Other studies are interaction-based, using occupant’s behaviors to adjust building energy
systems. Though many of these studies acknowledge the impact of exterior conditions,
nearly all fail to incorporate external influence factors, such as solar energy, into the final
models.
2.1 Building Energy and Efficiency
The worldwide energy crisis is an enduring problem. The U.S. is moving from a period of
inexpensive readily available energy to a period where energy is expensive and will need
to be budgeted accordingly (Hofman 1980). Fossil fuels are finite resources which
currently supply 81% of primary energy consumption. The use of these energy resources
are major contributors to CO ₂ emissions which have increased 43% in the last two
decades. The increase in these emissions has a direct effect on global warming and as a
result, collaborative global efforts to reduce energy consumption and CO₂ emissions are
critical for the future.

8

2.1.1 U.S. Energy Consumption
As of 2010 the energy consumed by the U.S. accounted for 19% of global consumption,
making it second only to China, in terms of the energy used by any country (Kelso 2011)
(Figure 1). This is an increase of 48% since 1980. Of the amount of energy used by the
U.S., 41% can be assigned to the building sector alone. This means that the energy used
by residential and commercial buildings within the U.S. alone accounts for 7% of global
consumption.

Figure 1 World and U.S. energy consumption
Source: Data adapted from Kelso (2011)

Heating, ventilation, and air conditioning (HVAC) operations are the main power
loads within a building, consuming 49% of the total energy used for building operation
(Kelso 2011) (Figure 2). Therefore, HVAC energy use in the U.S. alone is responsible for

3.43% of the entire global energy usage.
of total U.S. electricity expenditures. Because HVAC systems account for a
portion of energy consump
systems is an ideal target for improvement.
Figure 2 Building
2.1.2 HVAC Background

Efficient HVAC systems are the key to efficien
HVAC systems is to add or remove heat from the air. The secondary concern is to control
humidity levels, typically by removing moisture in the summer and adding moisture in
the winter. This is achieved through
to provide thermal control in buildings. Control of the thermal environment is a primary
Lighting
9%
Other
8%
Adjust to
SEDS
7%
Refrigeration
4%
Electronics
4%
Computers
2%
3.43% of the entire global energy usage. Buildings also account for 82% (or $302 billion)
of total U.S. electricity expenditures. Because HVAC systems account for a
portion of energy consumption, optimizing the efficiency of HVAC and associated
systems is an ideal target for improvement.
Figure 2 Building energy usage by percentage
Source: Data adapted from Kelso (2011)

Efficient HVAC systems are the key to efficient buildings. The primary purpose of
HVAC systems is to add or remove heat from the air. The secondary concern is to control
humidity levels, typically by removing moisture in the summer and adding moisture in
the winter. This is achieved through a variety of mechanical and electrical systems used
to provide thermal control in buildings. Control of the thermal environment is a primary
Water
Heating
12%
Cooking
3%
Wet Cleaning
2%
Space
Heating
37%
Ventilation
3%
HVAC
49%

9
Buildings also account for 82% (or $302 billion)
of total U.S. electricity expenditures. Because HVAC systems account for a substantial
tion, optimizing the efficiency of HVAC and associated

t buildings. The primary purpose of
HVAC systems is to add or remove heat from the air. The secondary concern is to control
humidity levels, typically by removing moisture in the summer and adding moisture in
of mechanical and electrical systems used
to provide thermal control in buildings. Control of the thermal environment is a primary
Space
Cooling
9%
Ventilation
3%

10

concern for practically all occupied buildings. This idea dates back thousands of years,
when such control may have provided means of survival during cold winters. In the
perspective of today’s world, thermal controls are much more complicated given that
thermal comfort and air quality directly influence occupant health, satisfaction and
productivity. The sensation of feeling hot or cold is not dependent on air temperature
alone. Thermal comfort is affected by heat conduction, convection, radiation,
and evaporative heat loss. In some cases, thermal comfort can be achieved by ventilation
alone, by increasing air movement to encourage evaporative cooling of the skin.
Understanding how a typical HVAC system operates is the first step to isolate
major energy consumers and locate target areas to reduce energy consumption. HVAC
systems typically include chillers and boilers which provide heated or chilled water to
one or more buildings. Air handler units (AHUs) mix outside air with returned indoor air
and cool or heat the mixed air according to a set point. The air is then distributed via
ducts and fans to thermal zones throughout the building. A thermal zone is an indoor
space or group of spaces with similar thermal loads. Each thermal zone is served by at
least one variable air volume (VAV) box, which will reheat the air, if needed, to meet the
temperature set point of that zone. Two major energy consumers in this process are the
AHUs at a building level and the VAV boxes at the thermal zone level (Li, Calis, and
Bercerik-Gerber 2012).
Standards must be recognized when evaluating building efficiency to maintain
thermal comfort and quality of indoor air. The American Society of Heating,
Refrigerating and Air Conditioning Engineers (ASHRAE) publishes standards addressing

11

energy efficiency, indoor air quality, refrigeration and sustainability of building systems
which are commonly accepted by architects and engineers and further implemented in
building codes. The ANSI/ASHRAE Standard 55, for example, refers to Thermal
Environmental Conditions for Human Occupancy. The purpose of the standard is “to
specify the combinations of indoor space environment and personal factors that will
produce thermal environmental conditions acceptable to 80% or more of the occupants in
a space (ASHRAE 1992).
2.2 Building Energy Conservation Strategies
The drive to reduce overall energy consumption in dynamic environments is an important
goal of effective building energy management. Although factors such as temperature,
lighting and air quality are regulated by standards, temperature and lighting can be
controlled within a specific range to save energy. There are many ambient factors that
affect the behavior of building occupants as well as how they perceive their surroundings.
These perceptions have a direct effect on productivity levels.
Many modern buildings today use HVAC systems that are programmed to operate
assuming maximum occupancy during operational hours. However, average occupancy
in office buildings has been observed to be only one-third of the design occupancy, even
during peak times of the day (Brandenmuehl and Braun 1999). Most HVAC systems
make adjustments throughout the day based exclusively on indoor air temperature and
humidity inputs along with assumptions about occupancy (Li, Calis, and Bercerik-Gerber
2012; Yang et al. 2011). These assumptions result in many buildings and unoccupied

12

spaces being over-conditioned and consequently, wasting energy and money in the
process (Erickson and Cerpa 2010).
2.2.1 Demand Driven HVAC
Demand driven HVAC operation is a strategy that aims to reduce HVAC energy
consumption by relying on occupancy information to adjust cooling/heating loads during
peak- and off-peak times. Typically, HVAC systems must wait for thermostats to detect a
change in temperature before responding. However, faster HVAC which respond to
changes in heat loads using three levels (low, medium, and high), have produced energy
savings of up to 50 percent in one simulation (Tachwali, Refai, and Fagan 2007). There is
a range of strategies that can be used to adjust parameters, such as temperature and
airflow, based on actual demand when operating HVAC systems (Table 1). The energy
savings associated with each strategy are strongly dependent upon building type. Several
studies indicate energy savings of 10-60 percent using different monitoring systems.
Table 1 outlines several strategies, methods, and area of focus for demand controlled
HVAC.
Occupancy information is important because it determines the heating and cooling
loads in specific areas of a building. It is defined as the number and identities of
occupants in a thermal zone and their associated activities occupancy (Li, Calis, and
Bercerik-Gerber 2012). Current building management systems use occupancy
information as a part of their functionality. However, most sensors installed in buildings
are generic and only control lighting, which does not have as large of an impact on
energy consumption. Also, the sensors involved are not accurate enough to provide

13

Table 1 Building energy management strategies
Study Building
type
Method Application Focus Energy
savings
Pavlovas (2004) Residential
(multi-
family)
Real time
monitoring
Reduce ventilation flow for
unoccupied areas
20%
(ventilation
energy only)
Agarwal et
al.(2010)
Educational Real time
monitoring
Maintain higher
temperatures in unoccupied
areas
10-15%
Ogasawara et al.
(1979)
Department
store
Occupancy
scheduling
Adjust outdoor air load
according to predicted
hourly occupancy
estimates.
20-30%
Yang et al
(2011)
Educational Real-time
monitoring
Minimum ventilation rates
per ASHRAE standards
based on occupancy
estimations.
15%
(ventilation
energy only)
Sun, Wang,and
Ma (2011)
High-rise Real-time
monitoring
Supplying airflow based on
occupancy
56%
Klein et al.
(2012)
Educational Occupant
preferences
Operating HVAC systems
based on preferences
13.6%
Erickson and
Cerpa (2010)
Office Energy
consumption
patterns
Using energy profiles and
trends to predict energy
needs.
20%
Tachwali, Refai,
and
Fagan(2007)
Multi-zone Real-time
monitoring
Hierarchical cooling rates
for HVAC based on quick
response according to
occupancy
50%
Erickson and
Cerpa (2010)
Educational Real-time
monitoring
Adjusting outside air
volume based on
occupancy
14%
Lo and
Novoselac
(2010)
Office Occupancy
control
Increasing flexibility of
control by dividing large
open areas
N/A
Bourgeois,
Reinhart, and
MacDonald
(2006)
Single
office space
Energy
consumption
patterns

Automatic lighting control
based on usage patterns
40%
Jazizadeh et al.
(2012)
Office Real-time
monitoring
Lighting system control
based on current
occupancy information
N/A

sufficient energy savings for demand-driven HVACs. Detection systems are designed to
function at various scales or levels. Some methods are only accurate for building level

14

occupancy detection, while other systems can only predict occupancy at the room level.
Building occupants have a range of activities that vary from stationary to mobile.
Because of the dynamic behaviors of a building’s occupants, building systems should be
able to actively respond to these behaviors.
Each strategy uses different types of occupancy information for input: Real-time
detection; occupancy scheduling; occupancy controls; and energy consumption patterns.
There are two types of real time detection strategies: individualized and non-
individualized. A non-individualized approach uses monitoring systems with binary logic
to determine if a space is occupied or not. The individualized method uses sensors and
monitoring devices to determine a specific count of persons in an occupied space.
Occupancy scheduling predicts patterns of movement and usage throughout a building
using predetermined schedules as inputs in Building Management Systems (BMS).
The occupancy control strategy records preferences of persons occupying a space
and adjusts HVAC to maintain those settings when the space is occupied. Learning trends
and creating energy profiles can be beneficial for more efficient HVAC by adjusting
energy needs based on consumption patterns. Although the accuracy of these systems
could be improved, any amount of energy savings is valuable compared to a building
without occupancy detection methods. The real time detection strategy proves to be the
most beneficial because HVAC systems are able to adjust for the actual demand instead
of the predicted demand.

15

2.2.2 USC BLEMS Project
Research has been conducted on real-time occupancy information as an input to demand
driven HVAC systems at USC. The Building Level Energy Management System
(BLEMS) project used Lewis Hall as a test bed to study the behavior of a building and its
occupants in real-time. Figure 3 illustrates how BLEMS communicates with other
systems as well as its own internal hierarchy. The objective of the research is to reduce
building energy consumption by integrating advanced occupancy detection while
maintaining thermal comfort levels (Yang et al. 2011).

Figure 3 BLEMS hierarchy (from Rossler 2012)

16

Real-time sensing of the environment allows HVAC systems to run based on
actual demand instead of estimated peak demand. This is accomplished by learning and
adapting to occupant and building behavior by balancing consumption with occupant
comfort levels. The self-contained system also recognizes existing systems and adapts to
new systems and communicates with them to support integration. BLEMS is hierarchical
because it supports the concept of a building composed of several zones; each containing
one or more rooms. The goal is to design improved occupancy detection systems which
are affordable, high-resolution, accurate, and non-intrusive.
One approach uses multiple sensors for occupancy detection and estimation
(Yang et al. 2011). The BLEMS sensor nodes use wireless technology to minimize
obstruction and allow for easy installation. Each sensor is installed in close proximity to
room entrances to detect occupancy. Each sensor node consists of seven sensors which
detect light, sound, motion, CO₂ concentration, temperature, relative humidity, motion,
and people passing by. Eleven values retrieved from the sensors are reported at one
minute intervals. These data are categorized into three sets of values; instances, counts
and averages. The instance values are readings of each of the seven sensors at the time it
is queried. The count variable tallies changes over the course of a minute from the motion
sensor and passive infrared (PIR) sensors. The average variable averages data from the
sound sensor in five second and five minute intervals. These data are time-stamped and
stored in a SQL database. The results have the ability to estimate the number of
occupants with up to 88% accuracy.

17

Another method for demand-driven HVAC addressed by the BLEMS research
team at USC uses radio frequency identification (RFID) to track mobile and stationary
occupants (Li et al. 2012). This individualized monitoring approach uses tracking tags
which are attached to occupants to monitor their coordinates and identities. The RFID
system has the ability to monitor multiple spaces simultaneously and reports the results in
real-time. The results provide a framework for demand-driven HVAC operations which
respond in real-time to the occupancy detection inputs. Average detection rates were
simulated with accuracies of 62% for mobile occupants and 88% for stationary
occupants. Through the integration of occupancy detection systems with demand-driven
HVAC operations energy consumption is expected to be reduced.
The BLEMS models and research at USC addresses many issues regarding
occupancy behavior and scheduling in efforts to program demand-driven HVAC systems.
-It is clear that for more efficient HVAC operation, the dynamic behavior of a building’s
interior environment must be anticipated. Yet, what about the exterior environment and
how those conditions affect the building envelope itself? In the previous research few
studies have accounted for how spatially-explicit environmental attributes regarding a
building’s location can be used to program more efficient HVAC systems. Through
research, Tachwali, Refei, and Fagan (2007) found that HVAC systems that respond
quicker to temperature changes proved to be more efficient. Climate can be predicted
similar to occupant behavior with high levels of accuracy. Sun patterns and solar energy
received from the sun are in fact very predictable. The integration and correlation of

18

interior and exterior environmental changes may afford new opportunities to further
reduce building energy consumption.
2.3 Solar Radiation
The sun is the primary source of heat and light and responsible for life on Earth.
Understanding the Sun’s relationship with Earth is critical for site planning, efficient
building design, and controlling unwanted heat gains. The Sun is a giant star and the
largest object in our solar system. The energy of the sun is a result of nuclear fusion that
occurs at temperatures ranging from 18 to 25 million degrees F (Stein et al. 2006). This
energy is released as electromagnetic radiation, at approximately 10 million degrees F
and travels 93 million miles before it reaches the Earth. The portion of the
electromagnetic solar spectrum that reaches the Earth is about 5% ultraviolet shortwave
radiation (0.01μm-0.4μm), 46% visible light (0.39μm-0.78μm) and 49% infrared
radiation (0.7μm-1,000mm) (Campbell and Wayne 2011).
Solar radiation (W/m²) refers to the amount of energy released from the Sun that
reaches the atmosphere measured by energy over surface area (Muneer 2004). Luminance
refers to radiation values received solely from the visible spectrum. Irradiation (Wh/m²)
is the total energy incident on a surface during a specified period of time. Instantaneous
incident energy on a surface is referred to as irradiance (W/m²) (Suri and Hofierka 2004).
The amount of energy that is received at the top of Earth’s atmosphere is
relatively consistent at 1366.1 W/m², known as the solar constant (Stein et al. 2006).
Approximately 30% of this radiation is reflected back into space by particles within the
atmosphere, clouds, and from the surface of the Earth, resulting in a global albedo factor

19

of 0.3. Around 19% of the solar radiation is absorbed by the clouds and dust within the
atmosphere. Insolation is the term used to describe the remaining 51% absorbed by the
Earth’s surface, 696 W/m², which is composed of 341 W of infrared radiation, 320.16 W
of visible light, and 35 W of ultraviolet radiation. Figure 3 illustrates the components of
incoming solar radiation.

Figure 4 Components of solar radiation
Source: Data adapted from Campbell and Wayne (2011)

20

Global radiation is the sum of three components: direct, diffuse and reflected
radiation (Súri and Hofierka 2004). Direct (beam) radiation is the largest component of
global radiation because it travels the shortest path, reaching the surface unobstructed.
Diffuse radiation passes through the atmosphere and is scattered by clouds and dust
before being absorbed at the surface. Reflected radiation is absorbed on non-flat surfaces
after being reflected from surface features. Shadows are exclusively the result of direct
radiation because all rays travel parallel to each other in the same direction; therefore, an
object is able to block all the rays at once.
The length of the radiation path is the primary factor in determining how much
radiation is received at the Earth’s surface. The effects of solar radiation with the Earth’s
atmosphere and surface can be grouped according to three factors (Súri and Hofierka
2004):
1. Global factors – Earth’s revolution and rotation (declination, latitude, solar hour
angle)
2. Landscape factors – Elevation, surface inclination and orientation, shadows
3. Atmospheric factors – Clouds, gasses and particles
2.3.1 Global Factors
At a global scale, the Earth’s rotation, (every 24 hours) and tilt have the largest impact on
global radiation. These factors cause variations in the length of atmosphere that radiation
must pass through before it is received at the Earth’s surface. The Earth's axis of rotation
is tilted at 23.5° and is also known as its declination (Stein et al. 2006). This tilt is

21

responsible for the seasonal variations (Figure 5). In the Northern hemisphere the Earth
tilts away from the Sun in December (-23.5°) resulting in fewer hours of sunlight and
longer paths by which direct radiation travels in non-perpendicular angles to the surface.
This is evident by winter’s low sun altitude and cold weather. The effect is the opposite
in June (+23.5°) where the sun reaches its highest altitude. Warmer weather is a result of
increased amounts of direct radiation traveling a shorter distance perpendicular to the
surface.

Figure 5: Annual elliptical path of the Earth around the Sun (from NOAA 2013)
The sun is the primary source of heat and light and when analyzing its effects on
building function and design, it is necessary to account for how it moves through the sky.
The Sun path refers to how the Sun appears to move through the sky with respect to a
point on the Earth’s surface. The angle between the horizon and the Sun’s position above
the horizon is the altitude angle which is 0° at sunrise and sunset (Stein et al. 2006). The
maximum altitude the Sun reaches during the day is called the solar noon. The altitude of
the solar noon varies throughout the year, reaching its highest point on June 21
st
(summer

22

solstice) and lowest point on December 21
st
(winter solstice). The altitude angle has a
significant effect on the amount radiation received at the building surface and in terms of
the design and efficacy of shading devices. The Sun’s path is unique for any given
latitude. The altitude at solar noon can be found for any location by subtracting degrees
latitude from 90° and adding (for locations north of the equator) or subtracting (for
location south of the equator) the Earth’s declination of 23.5°. Figure 5 illustrates the
Sun’s altitude angles for Los Angeles, California which has latitude of approximately 34°
N.

Figure 6 Solar altitude over a year based on Los Angeles, CA
23.5
23.5
Dec. 21
st
(winter solstice)
Mar. 21
st
, Sep. 21
st

(equinox)
June. 21
st
(Summer solstice)
Local Latitude 34°
32.5°
56°
79.5°
47°

23

The azimuth angle, or solar bearing angle, is the angle along the horizon between
the position of the Sun and true south. In the Northern Hemisphere the sun rises due
North of East in the summer, Due East at each equinox and due South of East during the
winter. The azimuth angle is significant when considering building orientation, analyzing
building exposures and reviewing shadowing angles (as will be discussed later).
The Sun path charts represent the three-dimensional characteristics of the Sun’s
path throughout the year onto a two-dimensional surface in Cartesian coordinates (Stein
2006). A rectilinear Sun path chart for Los Angeles, CA can be found in Appendix A.
This type of chart is a graph that is created from an observer’s perspective where the
vertical center is an observer looking due south. The azimuth is plotted along the
horizontal axis and altitude on the vertical axis. The horizon, a horizontal plane at the
observer’s eye level, is represented at the line at the bottom of the chart. It is important to
note, that the Sun’s path is only plotted for the months of January through June because
after the summer solstice the Sun path repeats itself for July through December. The
emphasis on the South orientation calls attention to the characteristics of receiving more
sun in winter and less sun in any other orientation.
2.3.2 Landscape Factors
At local and regional scales the topography is the main factor in determining distribution
of insolation. Variations in elevation, surface orientation (slope and aspect) and shadows
cast from surface features result in high spatial and temporal differences in local radiation
values. Variations within the urban surface result in high levels of heterogeneity for

24

surface radiation at spatial and temporal scales (Fu and Rich,2000). There are thousands
of weather stations around the world which monitor solar radiation yet, for urban areas
point specific measurements are not useful for accurate insolation data because of the
complexity of the landscape.
2.3.3 Atmospheric Factors
During dry and clear sky conditions, at solar noon; global radiation is at the maximum
value for a given day and location. Clouds are the largest blockers of radiation. As cloud
cover increases, the percentage of global radiation resulting from direct radiation
decreases and the percentage resulting from diffuse radiation increases. Uniform overcast
sky (UOS) refers to a consistently cloud covered sky. In this scenario direct radiation is at
its lowest value and diffuse radiation accounts for the largest amount of global radiation
(Hofierka and Zlocha 2012). Moisture within the air, measured as humidity, has a direct
effect on temperature resulting from increased levels of solar absorption.
Aerosols and dust particles within the atmosphere, resulting from pollution, may
reflect and absorb radiation and thereby impact the amount of total insolation at the
Earth’s surface as well. Levels of pollution in urban areas have a drastic effect on
sunlight increasing the amount of scattering and absorption of diffuse radiation by 40-70
percent and reducing the amount of direct solar radiation by 30-50 percent (Santamouris
2001). For example, research has indicated that Los Angles receives approximately 50
percent less sunlight compared to surrounding rural areas.

25

2.4 Solar Radiation and Heat Transfer Effects in Buildings
Thermal comfort is a function of personal health factors, air movement, relative
humidity, ambient air temperature and mean radiant temperature. Heat transfer in
buildings occurs through convection, conduction, and thermal radiation through the roof,
walls, floors and windows. The flow of heat through a building varies by season and by
path of heat flow (materials, intentional and unintentional air pathways). Thermal
radiation moves from the warmer surface to a cooler one. The main source of heat
transfer is radiant energy received from the Sun. Solar radiation that is absorbed heats the
surface and is no longer solar energy. The absorbed energy is exchanged through
conduction with the layer directly behind the exterior surface.
The effects of solar radiation heat transfer occur at the roof, the walls and via the
windows. The U-factor is a coefficient that is used to measure the thermal transmittance
of a material, expressed in Btu/h ft² °F (Campbell and Wayne 2011). Low U factors
indicate a better insulation factor and therefore the decreased ability to transfer heat.
Opaque building materials such as walls, floors, and roofs are usually insulated.
Insulation and building materials have the greatest impact on how heat is transferred and
stored within a building. The roof is the uppermost part of any building and is the main
element impacted by solar radiation. It receives the most sun during the day and
throughout the year. The temperature throughout the day can vary greatly making it
susceptible to heat transfer into the building and increasing radiative heat transfer. When
a floor is exposed to outside air, the heat transfer properties are similar to that of roofs
and walls.

26

Windows are the most notable and predictable site for thermal radiation. They
transmit solar heat into a building which is favorable in the winter and unfavorable in the
summer. Windows allow thermal radiation to pass in a building during daytime and out
of the building during the nighttime. The U factor for windows is generally high because
they are difficult to insulate and store a minimal amount of heat. The effects of this heat
transfer are varied by insulated glazing, internal and external shading and orientation.
Solar heat gain in most building models will be greatest at the windows. The wall to
window ratio is also important to consider in analyzing solar heat gain.
The objective of solar radiation control is to decrease the cooling load on a
building. The intensity of summertime direct solar radiation on horizontal surfaces such
as a large area of low slope roof makes the roof the primary target for solar radiation
control.
Most research in the field of solar radiation effects on buildings has aimed to
evaluate the potential of solar collectors for energy capture. In this context, models and
calculations used only regard the roof surface of a building, and do not require three-
dimensional analysis of the entire building envelope. The following case study regarding
passive heat gains evaluates solar impacts on a temporal and spatial scale, but most
importantly it acknowledges the entire building including the exposure of the vertical
surfaces.

27

2.4.1 Case Study: Urban Density and Passive Solar Heat Gains
The basis of passive solar heating involves using the direct gain of solar heat through
windows, usually south facing, to reduce heating costs during colder times of the year.
Active solar heating refers to using solar collectors to store solar energy.
Research performed in northern Europe analyzed the concept of passive solar gain
and how building energy consumption is affected by surrounding context (Strømann-
Andersen and Sattrup 2011). The context which is the variable in this study refers to the
urban canyon, which is measured as a ratio of building height to width of space between
the building being studied and the next building. The lowest ratios represent areas that are
typical of urban squares, and the highest ratios represent conditions that are typical of
alleyways and narrow boulevards. Energy consumption was examined based on five
primary needs: Heating load, cooling load, lighting, ventilation and Domestic Hot Water
(DHW).
The results indicate an increase in general energy consumption as the density of
the surrounding environment increases. Cooling demand was shown to be reduced due to
overshadowing in warmer seasons, but the reduction in solar heat gains in cooler seasons
caused an increase of heating costs. Though artificial lighting is highly variable, the
estimation model indicates that energy usage doubles when comparing the unobstructed
model to even the lowest density ratio. Lighting energy usage increased six times when
compared to the highest density model. The study also compared energy usage depending
on the building floor height. The results showed that the building energy consumption
increases the closer to the ground a level is located. Comparing building orientations, the

28

results showed that unobstructed context favors buildings oriented in a North/South
direction, while East/West orientations were more efficient in increased urban density
models.
2.5 Solar Modeling and Building Simulation Tools
Solar radiation is a very complex phenomenon. Methods and models used to understand
and analyze the Sun’s energy range from simple to complex. Solar radiation models
provide the means for understanding the spatial and temporal variation of insolation over
various landscapes under varying conditions. Building simulation tools offer cost-
efficient methods to evaluate factors that affect a building’s performance. It can be
assumed that the less energy a building uses the more sustainable the building will be.
Several software programs were reviewed before selecting methods that will produce the
most ideal results for the thesis case study. Table 2 summarizes the characteristics of the
software.
2.5.1 Calculation-Based Models
FORTRAN, an acronym for FORmula TRANsaltion, is one of the most widely used
programming languages for engineering applications (Muneer 2004). Having been in use
for over 55 years, there is a large number of programs that have already been developed.
Muneer (2004) describes a series of FORTRAN programs that evaluate virtually all
aspects of solar radiation and illuminance computations. As described before, there are
several complex factors that need to be considered for the dynamic evaluation of solar
energy .FORTRAN has the ability to calculate such complex algorithms quickly, for a

29

Table 2 Summary of software characteristics used for solar modeling and building
energy analysis
Software Level of
knowledge
Input Strength Weakness Availability
Calculation based
FORTRAN Advanced Solar algorithms Quick
calculations
Output is not
graphical
Requires many
separate
programs for
full
implementation
Energy
PLUS
Intermediate Hourly weather
files plus Building
characteristics in
database format
Accurate
detailed
simulations
Text input Free download
Two-dimensional
PARASOL Fenestration
knowledge
Site and building
specifications
Comprehensive
analysis of
window systems
Only focuses
on window
systems
Free Download
ArcGIS
Solar
Analyst
Basic GIS
knowledge
DEM
weather station data
Fast and
accurate
calculation
2-
dimensional
DEM data is
not
publically
available at
high
resolutions
ArcGIS
software
license with
spatial Analysis
extension
Grass GIS
r.sun
module
GIS
knowledge
required
DTM Best for use
over large areas
2-D Data,
Preparation
for in input.
Free-open
source code
Remote
sensing
Basic Aerial imagery Most accurate
Solar analysis
for a given time
and location
Limited
Temporal
scale
Location
specific results
are available at
select city
portals.
Green
building
studio
BIM
knowledge
3-D CAD file Entire building
energy analysis
Process is
complicated
Requires
license
Three-dimensional
SketchUp Basic Building
dimensions/location
Simple to use Shadow
analysis only
Free download
Grass GIS
v.sun
module
GIS
knowledge
and Script
development
raster, vector-voxel
data formats
3-D

Not fully
developed
Limited
output
Not publically
available
Ecotect Basic CAD
knowledge
3D design data
Weather Data
Tables
Whole building
analysis in 3D
Flexible options
Varying data
requirements
Time
consuming
for detailed
models
Free license for
students

30

range of scenarios, at variable spatial and temporal scales. The files created can be
embedded into infinite simulations or external energy simulation programs.
Although, FORTRAN code, in itself, was developed for easy understanding,
intermediate knowledge of computer programming, as well as a variety of software
programs, is required to edit, compile, and run the aforementioned simulations. While
very efficient and accurate, the output of FORTRAN programs are primarily
computationally bound, i.e. used to generate sets of numbers, providing no direct visual
analysis. However, many of these solar models have been tested over time and evaluated
for accuracy, requiring little need to edit actual source code and algorithms. Fortunately,
many energy simulation programs incorporate these solar models within their software.
The U.S. Department of Energy’s Energy Plus is a complete building analysis
program that calculates energy performance and life cycle costs of operation. One of the
main strengths is that is can be used to analyze energy efficacy given specific designs or
new technologies. However, a high level of knowledge is required along with advanced
training to use this program effectively.
2.5.2 Two-Dimensional Modeling
The Solar Analyst module in ArcGIS uses georeferenced digital elevation models
(DEMs) to calculate radiation (Wh/m²) at the surfac e and locals scales (Fu and Rich
2000). Solar radiation analysis tools are available with the ArcGIS software to analyze
area or point radiation (Esri 2008). The point specific model calculates insolation at a
location based on surface orientation and visible sky. Local topography is taken into
account based on ground-based observations. While point specific models are highly

31

accurate for a given location, an enormous number of calculations would be required to
determine insolation over a landscape, and furthermore would prove difficult in an urban
landscape.
The area-based model considers insolation over a geographic area by calculating
surface orientation and shadow effect data from input DEMs. The results are only as
accurate as the resolution of the DEM. High resolution DEMs are not always publically
available and are typically not cost-effective to create within the scope of a project
analysis. The solar flux model simulates how shadow patterns influence direct radiation,
using the hillshade function at specific points in time. In addition to long computation
time, there is relatively little flexibility in terms temporal scale. Also the results from
using these methods are only available for 2-dimensional surfaces. There is a 3D Analyst
extension, but the functionality of this product is currently limited to visual analysis.
GRASS (Geographical Resource Analysis Support System) is a free GIS
developed in an open source environment used for geospatial data and analysis (GRASS
2013). Different modules and scripts can be added to this program to perform varying
kinds of analyses. The r.sun module for example was developed and is primarily used to
estimate photovoltaic potential on roof tops (Súri and Hofierka 2004). It uses raster data,
such as Digital Terrain Models (DTMs) for input, output, shadowing algorithms and solar
radiation calculations. It calculates all three levels of solar radiation (direct, diffuse, and
reflected) for clear and real sky conditions. The r.sun module is ideal for measuring data
over large areas with complex terrain. A drawback of this software is that complex data
preparations are required to compile the inputs. In addition, the r.sun module is only

32

capable of measuring values in 2-dimensions which is only effective for measuring solar
insolation on land surfaces (terrain) and rooftops (Hofierka and Zlocha 2012).
Solar maps have been created for several cities throughout the US to estimate the
solar potential of areas within the city. These maps are generated from a combination of
aerial imagery, solar potential software, and solar engineering models (Dean et al. 2009).
Two kinds of input are needed to generate a solar map; topographical and meteorological
data. Topographical data can be obtained from Light Detection and Ranging data
(LiDAR) imagery which is used to create a surface model. Solar potential software,
ArcGIS for example, is then used to determine the amount of solar insolation which
strikes the ground over the course of the year. Building information extracted from
LiDAR data is used to create Digital Surface Models (DSMs) which take into account
shading obstructions, roof tilt and surface area. This information is overlaid onto the
resulting solar insolation model. The final product is a two-dimensional map that
represents the amount of solar insolation received on the top of surface features. The Los
Angeles County Geoportal provides access to a solar map for the entire county at
http://solarmap.lacounty.gov/ for example.
Although the methods used to create solar maps may be very accurate, data is not
always readily available, and can be expensive to obtain. Solar maps are primarily used
as a source for determining the placement of solar panels on rooftops. Also, this type of
solar map returns only one final value: Annual total radiation and further temporal
adjustment of the results are possible.

33

2.5.3 Three- Dimensional Modeling
Trimble SketchUp is a three-dimensional modeling software program that is simple to
use and available for free download. Many real-life 3-dimensional models are linked to
Google Earth via .kml files. The inputs of geographic coordinates are useful for
evaluating realistic shadow effects. SketchUp makes basic three-dimensional modeling
simple, but cannot handle the complex, detailed models that will be used in this study.
Many third party plugins are also available but may not be valid or stable.
The v.sun module, for example, is based on the methodology used in the r.sun
module but has the added capability to process three-dimensional vector solar data
(Hofierka and Zlocha 2012). Spatially variable solar parameters and shadowing
algorithms are input in raster/voxel formats. Using a combined vector-voxel approach,
the volume structure of a region (voxel data) is divided into smaller polygons which
define the distribution of 3-D vector objects. The module has two modes for calculation.
Mode 1 is used for instantaneous calculation of solar incident angles (degrees) and solar
irradiance (W/m²) which is output in a 3-D vector-b ased format. Mode 2 uses the 3-D
vector based data to provide daily sums of solar radiation (Wh/m²) and daily direct-sun
duration (minutes). Further use of the v.sun module requires advanced knowledge of
computer languages and scripting. The basic operation of this module could not be tested
because it has not been made publically available by its developers.
PARASOL is a basic energy simulation tool used to evaluate solar protection
devices and glazing types. The output is monthly totals of direct solar energy
transmittance of the sun shade or window system. It also provides an evaluation of the

34

influence of the shading device on building energy and performance. Although simple to
use, this software must be used in addition to other tools and models because it provides a
very limited portion of a complete building energy analysis.
Green Building Studio is a building information modeling program developed by
Autodesk. It is a web service that generates detailed input files for energy simulation
programs. It links architectural BIMs and 3D CAD designs with energy, water, and
carbon analysis. The required input is a .gbxml file type which can be generated from
BIM modeling software. The output is extremely detailed and accurate, but is time
consuming to generate. A license is required to run this service unless a free trial is used
for a specific period of time.
Ecotect is a comprehensive environmental and building energy analysis tool. This
software, adapted by Autodesk, performs complete building energy analysis similar to
other building energy software described, but is exceptional because it provides an
advanced 3D modeling interface. It allows the user to analyze interior and exterior factors
and the impact they have building performance. This program allows for wide-ranging
visual and analytical outputs for analysis of, solar, lighting, thermal, wind and acoustic
impacts on building performance. Valuable feedback can be received from scalable
inputs ranging from simple massing models to complex cityscapes. The level of detail
and accuracy can also be increased by assigning material properties to construction and
defining occupancy scheduling. There is also high flexibility in the detail of the outputs
including various temporal scales (Autodesk 2012). However, the computation
requirements are time-consuming at detailed levels, and there are substantial data inputs

35

are required for accurate analysis. Ecotect is also limited in its abilities because it does
not account for vegetation within the landscape which can have a significant impact on
building performance.
Notwithstanding these shortcomings, Ecotect was chosen as the most suitable
software for this thesis because of its diverse capabilities and flexibility. The three-
dimensional modeling capabilities, for example, allowed for easy visualization of the
geometry and components of the buildings that were studied. This software also allowed
further output in the form of tables, images and graphs, which were beneficial to the
research at hand. After the model was defined for this study, further research can be
completed using the same model for analyzing different aspects of building efficiency.
The next chapter describes this modeling software and the data that were used for the
work at hand in greater detail.

36

CHAPTER THREE: METHODS AND DATA SOURCES
Building simulation and solar modeling takes time. Accurate results depend on precise
inputs for realistic results. Ecotect offers a several tools to perform building energy
simulations. Since this thesis primarily deals with solar energy impacts on buildings, only
the solar access tool will be described in this chapter. The essential inputs for the solar
access analysis tool include location, weather and building construction data. To observe
the effects of temporal changes, solar analysis will be conducted for each of the four
seasons. The effects of spatial changes on a building’s performance will be evaluated by
comparing the results over three progressive phases, which will be discussed in more
detail below.
3.1 Study Area
The case study used for this thesis involves an existing four-story educational structure
titled Ralph and Goldy Lewis Hall on the USC’s University Park campus in Los Angeles,
California (Figure 6). The building houses the Sol Price School of Public Policy and has
about 20,000 ft² of space, including classrooms, la bs, offices and lecture halls. This
structure was selected because of the availability of data and efforts to integrate this data
with current BLEMS research at the same location, as was described in Chapter 2. The
building has the characteristics of a typical educational facility. It is a relatively new
building which was built in 1996.

37

Figure 7 Lewis Hall sketch (North Façade)
Source: Data obtained from USC Facilities Management adapted using Autodesk REVIT
(2011)
The building is located on the southeast part of the campus and is oriented 28°
east of north, aligning with the layout of the majority of the buildings on the University
Park campus (Figure 7). The impacts of the immediately adjacent buildings are
considered for the shadowing effects that they may present for the building of interest.
The area adjacent to the southern portion of Lewis hall was formally the location of the
University Club that was demolished in 2012. The new Quantitative Social Sciences
building that is being constructed here will also be considered for a portion of the
analysis.
Understanding the climate and location of the area to be analyzed is important for
setting up and understanding the many calculations performed by Ecotect. The city of
Los Angeles is bordered to the east by the Santa Monica Mountains and to the south and
west by the Pacific Ocean. The climatic conditions can be characterized as a Subtropical-
Mediterranean climate, with average monthly temperatures ranging from 57.4 to 75.6 °F.

38

The highest precipitation occurs from December through March with an annual average
total precipitation of 14.93 inches per year. Snow is a very rare occurrence in the Los
Angeles area, with an exception being the high elevations of the surrounding mountain
ranges. Annual sunshine averages more than 3,000 hours. The monthly averages range
from 219 sunshine hours in December to 364 sunshine hours in July.

Figure 8 USC campus site map
Source: Data obtained from USC Facilities Management adapted using Esri ArcMap10

39

3.2 Workflow
For the most accurate solar simulation model, data must be integrated to include all three
factors of solar analysis: global, landscape and atmospheric. In addition, the building’s
geometry must be carefully and accurately input to achieve realistic results. The accuracy
of the analysis is dependent on the user’s ability to properly build the structure in
question. The focus of the model is on the building envelope, i.e. the components which
contain the interior spaces and are exposed to outdoor elements. For the purpose of this
study, the envelope components to be evaluated are the windows and walls of the vertical
surfaces. Data is obtained from several sources and processed using the steps illustrated
in Figure 8.
3.2.1 Data Processing
First, shape files which contain site context data including the building footprints for all
structures on the campus were obtained from the Facilities Management Services group
at the University of Southern California. The footprint’s projections were converted from
the Los Angeles County zone in the State plane coordinate system to a spherical global
coordinate system in order to obtain the most precise global coordinates for building
locations and orientations. The result was exported in CAD .DXF files and imported into
Autodesk’s REVIT software program.

40

Figure 9 Data processing flow chart

41

In order to build an accurate building model, details regarding the building
structure and floor plans were additionally loaded into Revit. The floor plans were
aligned to the building footprints from the shape file and then extruded to the proper
height to build a three-dimensional model of Lewis Hall and surrounding buildings. The
result is a spatially explicit three-dimensional model. More intricate details such as
window geometries and locations were also retrieved from floor plans and added to the
model. General values for building materials were added to each building element of
concern. For windows, values for aluminum framing with double glazing were input. The
structure of the walls is concrete block with steel framing and curtain walls.
The resulting three-dimensional model was next exported into the Trimble
SketchUp software program to create a shading heliodon for preliminary shading
analysis. A shadow analysis can be used to evaluate overshadowing effects at different
times of the day and at different times of the year to evaluate effects of the surrounding
urban environment. A heliodon is created for key times, at four-hour increments, on key
dates throughout the year and the results of this part of the analysis can be found in
Appendix B (Figure 11). Shadows are solely the product of direct radiation and therefore
shadow patterns will assist in overall knowledge of how and where direct bean sunlight is
hitting the building. Long shadows are the result of a low solar altitude, when direct
radiation values are lowest. Conversely, short shadows are the result of a high solar
altitude which is when direct radiation values will be at their highest. The three-
dimensional model of surrounding campus buildings provides a visual summary of the
impact of overshadowing from neighboring buildings.

42

The three-dimensional model was then exported and converted into a .gbxml.
This file type contains three-dimensional BIM data concerning the volumes of rooms and
material types. These data were then imported into the Ecotect software program. The
building was divided into 60 thermal zones. The aggregation of spaces into thermal zones
is dependent on the locations of VAV thermal controllers as well as physical boundaries.
Some thermal zones contain several spaces while others contain only a single room. The
thermal zone layout is the same used in previous BLEMS studies at the same location.
The auditorium on the first floor is an exceptionally large space and therefore this space
was divided into two thermal zones. The various Lewis Hall floor plans and thermal zone
divisions can be found in Appendix A (Figures 22-24). An internal zonal adjacency
calculation was performed in Ecotect to ensure all spaces were aligned correctly.
The downtown Los Angles weather station (KDQT) is located directly on the
USC campus at latitude 33.9° N, longitude 118°W (Figure 7). Data from the weather
station including precipitation, humidity, average daily temperature, direct radiation and
diffuse radiation can be downloaded from the US Departments of Energy website, in an
.epw file format, and subsequently loaded into Ecotect. The weather tool extension
converts the data into a weather (.wea) file format in order to be used in Ecotect. This
data allows atmospheric factors to be accounted for in the solar analysis.
3.2.2 Solar Analysis Input Stage
The components comprising the building’s envelope were isolated so that the solar
analysis could be calculated for only the areas of concern. These areas are those that are
potentially exposed to solar insolation and are adjacent to a thermal zone: Walls,

43

windows, and exterior floors. The remaining elements such as the roof, columns, the
arcade walkway, and exterior stairways were only considered for their shadowing
impacts on thermal zones.
Once the building model was completed and all of the parameters were entered,
the solar analysis calculations were performed using the solar access tool in Ecotect. The
solar access analysis tool requires several inputs to achieve specific customized results.
These inputs and the values that were are summarized in Table 3. The solar access
analysis tool calculates the amount of solar radiation insolation on surfaces of concern
within the model. For this analysis, the total solar radiation which is the sum of the direct
and diffuse solar radiation was evaluated. Reflected radiation is typically insignificant
and is not considered within this program.
Table 3 Input values for Ecotect solar access analysis tool
Input Selection Explanation
Calculation Incident Calculates total radiation( Sum of direct and
diffuse) falling on objects
Time Period 8AM-8PM Calculations are carried out each hour with the
range.
Period Season or All Year Determines values based on Sunrise to sunset
for given location. Will be completed five times
total for each phase.
Period based values Average Daily Values Calculates total radiation for each Period
specified and divide by the number surface over
given period
Object selection Use selected objects By isolating selected objects, radiation values
will only be calculated for objects that are part
of the analysis.
Object
Overshadowing
Perform detailed shading
segmenting sky into
5°x5°subdivions.
Creates a shading mask for each object to
determine which parts of the sky are visible and
what percentage of object is in shade.

44

Solar calculations are carried out each hour for 12 hours, between 8 a.m. and 8
p.m., for each day of the evaluation. Values any time before sunrise or after sunset are
ignored for that given day. Solar analysis was conducted for five separate time periods,
each of the four seasons plus the whole year. For review, the seasonal time periods used
were as follows: Winter, 21 December to 19 March; Spring, 20 March to June 20,
Summer, 21 June to 20 September, and Fall, 21 September to 20 December. The annual
value averaged the daily values over a typical 365-day year, from 01 January to 31
December.
Shading effects were the most complicated and most time consuming portion of
solar access analysis. The Sun must be considered as a direct point source in addition to
the entire sky dome, which approximates a dispersed hemispherical source (Marsh 2007).
As the Sun moves through the sky, calculations become even more complex. The basis of
this calculation is to find whether or not a specific object is shaded at any particular
moment. This is accomplished by created a ray trace from the object to the Sun and
checking for obstructions. When the objects in question are surfaces, they must be
subdivided by a grid because only a fraction of each surface may be obstructed. In this
case study, a 5x5 grid was chosen for medium accuracy which allowed each surface to be
sampled 25 times. The sky dome was divided into 5 degree segments in both azimuth and
altitude to produce a total of 1,296 segments (Figure 9). This level of detail was needed to
account for the sun’s position as it moves through the sky.

45

Figure 10 Sky dome in 5° by 5° subdivisions (from Marsh 2007)
Three layers of data were stored within each sky segment: shading from external
obstructions; angle of incident effects; and radiation reflected from surrounding objects
(Marsh 2007). Figure 10 illustrates a shading mask for a southwest facing wall on the
second floor of Lewis Hall. After the appropriate weather data is loaded, diffuse and
direct radiation values can be filtered and included used for the final calculations.

Figure 11 Example shading masks in stereographic view (left) and three
dimensional views (right)
Source: Ecotect 2011

46

3.2.3 Phases of Analysis
The analysis was completed in three separate phases, progressively adding context to the
prior phase (Figure 11). This approach was employed to demonstrate how spatial changes
in the surrounding landscape can affect solar insolation values.

Figure 12 Progression of Phases: phase01 (top), phase02 (middle), Phase03 (bottom)

47

Phase 01 will evaluate Lewis Hall as a standalone structure in an unobstructed
context. The resulting solar radiation values were based solely on the building’s
orientation, geometry and roof overhangs. This provided a basis for comparison, for
understanding how landscape changes impact the solar insolation at the building’s
surfaces.
Phase 02 included the current site context provided by neighboring buildings. The
proximity and height of neighboring buildings may have an overshadowing effect which
will impact radiation values depending on the Sun path in each season. The results from
this phase will be more realistic than those from Phase 01 as they represent current
conditions.
Phase 03 will include the current site context plus the new Quantitative Social
Sciences Building, which is currently under construction. The new building will be
located on the site 11.5 m (37 ft) to the south of Lewis Hall and will stand at 26.5 m
(87ft) in height, nearly twice the height of Lewis hall. The close proximity and large
vertical height will greatly impact overall radiation values and especially the Southern
exposure.
The roof is the main element impacted by solar radiation. It is considered part of
the external envelope, but will be analyzed separately in all three phases because the
spaces that are directly adjacent to the underside of the roof are not typically occupied or
temperature controlled and do not impact any specific thermal zone within the building.
Variations of solar energy at the roof will represent impacts on overall building energy
efficiency and use and not any thermal zone in particular.

48

CHAPTER FOUR: RESULTS
This chapter describes the results of the solar access analysis using Ecotect. Seasons are
compared to analyze the effects of temporal changes and the three progressive phases,
previously outlined, are also compared to demonstrate the results of nearby changes on
solar insolation values.
The Ecotect solar analysis tool was used to generate the average daily incident
solar radiation values for each of the four seasons plus annual values. This process was
run 15 times; five times for each separate time period and three times for each of the
three phases. In each variation, average solar radiation values were calculated for 210
objects which comprise the building’s envelope. The radiation values for each object
were weighted according to the surface area of the building component and aggregated to
produce totals for the corresponding zones. Most zones include both window and wall
type objects, with the exception of a few which contained only window or wall object
types. The weighted average values for the windows and walls of each zone are
compared across each season.
The building’s envelope primarily consists of windows, walls, horizontal floors
and roof areas. Roof areas and materials are considered separately because they have an
impact on overall building energy. Horizontal floor surfaces, found in the terraced areas
of the building are assigned to the thermal zone immediately below the surface. They are
grouped together with wall objects because they are about the same thickness and are
composed of similar opaque materials. Incident solar radiation at the walls provides a
good indication of how much energy will be absorbed by opaque surfaces of the building

49

envelope. Solar energy values at the windows were evaluated separately because these
values will be a good indication of how much energy is transmitted into the building and
will have a greater influence on heat gains and resulting internal zone temperatures.
Each object type within in each thermal zone was assigned to its corresponding
thermal zone by object type. The main façade is rotated 28° East of North and therefore,
project North in the Floor plans is considered a northeastern solar exposure. Each zone
corresponds to one of four orientations: North, east, south, or west based on its primary
exposure to the Sun (Figure 13). Zones located within the corner areas of the building,
are exposed to two orientations, and therefore assigned to the orientation which is most
dominant for that zone. The detailed results for each zone can be found in Appendix B.

Figure 13 Building orientations (28° East of North)

50

4.1 Phase 01 Results
The values in Phase 01 are highest of the three phases because there are no obstructing
site contexts and resulting overshadowing effects that were into account. Any
overshadowing effects result solely from the building’s orientation, geometry, global
position, and atmospheric factors. The roof overhang provides shading to zones on the
third floor, most noticeably in the summer months when the Sun’s elevation reaches its
maximum. The values for zones with surfaces within the alcove in the southern part of
the building are lower due to the shading effects from the building’s own geometry.
Figure 14 shows the results for each season aggregated by orientation. Lewis Hall
in its unobstructed context receives the highest amounts of radiation at the roof with the
exception of the winter months where the southern façade received the most radiation.
This result is almost opposite in the summer months where the southern façade receives
the second least amount of radiation and high amounts are received at the roof. The
northern exposure receives the lowest amount of solar energy throughout all four seasons,
as expected, although it is interesting to note the values at the northern exposure are very
close to values at the southern exposure in the winter season. This is a result of the
highest solar angles being achieved at the beginning of summer and less solar energy
reaching the southern façade compared to other seasons. Overall, solar radiation is
received fairly consistently at the eastern and western exposures throughout the year,
with slight variations due to atmospheric conditions. It is interesting to note that values in
the winter for the southern exposure are only slightly less than the values for the western

exposure during the summer. This is a result of sun angles as well as the much greater
surface area exposed at the southern
Figure 15 depicts the results for
three floors within Lewis
alcove and is also not impacted by the overhang on the roof resulting in the highest
amount of radiation for all seasons.
winter season, which can be attributed to decreased daylight hours in the winter time.
However, the Sun’s low azimuth angles in the winter
the first and second floor wall v
the winter months account for the highest seasonal values for the third floor, which
results from the roof overhang having a lesser impact, when the sun’s elevation is at its
exposure during the summer. This is a result of sun angles as well as the much greater
the southern end of the building.
Figure 14 Phase 01 results by orientation
Figure 15 depicts the results for Phase 01 where zones are aggregated by
three floors within Lewis Hall. The first floor contains no zones located in the south
alcove and is also not impacted by the overhang on the roof resulting in the highest
amount of radiation for all seasons. The lowest values for the first floor are during the
winter season, which can be attributed to decreased daylight hours in the winter time.
the Sun’s low azimuth angles in the winter, results in very similar values for
and second floor wall values, 1212 and 1213 Wh/m², respectively. Conversely,
the winter months account for the highest seasonal values for the third floor, which
from the roof overhang having a lesser impact, when the sun’s elevation is at its

51
exposure during the summer. This is a result of sun angles as well as the much greater

e 01 where zones are aggregated by one of
The first floor contains no zones located in the south
alcove and is also not impacted by the overhang on the roof resulting in the highest
owest values for the first floor are during the
winter season, which can be attributed to decreased daylight hours in the winter time.
in very similar values for
respectively. Conversely,
the winter months account for the highest seasonal values for the third floor, which
from the roof overhang having a lesser impact, when the sun’s elevation is at its

lowest elevation. Overall,
year due to the fact that it is
Appendices B and C provide graphic and tabular results for each individual
thermal zone. Table 4 highlights the zones, walls and windows, which receive the highest
and lowest values in each season. For better understanding, this table also
location of the zone object within the building, by floor and primary orientation
receives the highest radiation during spring and fall seasons
floor with a southern exposure.
at its walls, which may seem
inspection, it can be noted that
not shaded and subject to a large
of Zone 39 receive the most radiation during the
lowest elevation. Overall, the third floor receives the least solar radiation throughout the
it is impacted by the overhang of the roof.
Figure 15 Phase 01 results by floor
Appendices B and C provide graphic and tabular results for each individual
highlights the zones, walls and windows, which receive the highest
each season. For better understanding, this table also
the zone object within the building, by floor and primary orientation
receives the highest radiation during spring and fall seasons, given its locat
floor with a southern exposure. During the summer, Zone 2 receives the highest radiation
at its walls, which may seem surprising given its northern exposure. Upon
inspection, it can be noted that Zone 2 has a large exposed horizontal surface, which is
not shaded and subject to a large amount of direct sunlight in the summer. The
one 39 receive the most radiation during the warmer months, due to its location on

52
eceives the least solar radiation throughout the

Appendices B and C provide graphic and tabular results for each individual
highlights the zones, walls and windows, which receive the highest
each season. For better understanding, this table also identifies the
the zone object within the building, by floor and primary orientation. Zone 9
location on the first
, Zone 2 receives the highest radiation
Upon closer
horizontal surface, which is
ummer. The windows
due to its location on

53

Table 4: Summary of results by BLEMS zone
Object type Phase01 Phase02 Phase03
Zone
(Floor,
Orientation)
Value Zone
(Floor,
Orientation)
Value Zone
(Floor,
Orientation)
Value
(Wh/m²) (Wh/m²) (Wh/m²)
Winter
Walls Max 37 (2,S) 2995 37 (2,S) 2959 29 (2,E) 1851
Windows Max 37 (2,S) 2998 37 (2,S) 2961 58 (3,S) 1975
Walls Min. 43 (3,N) 296 43 (3,N) 294 52 (3,S) 138
Windows Min. 22 (2,N) 347 22 (2,N) 154 52 (3,S) 121
Roof 2671 2665 2405
Building 1132 1105 710
Spring
Walls Max 9 (1,S) 2514 9 (1,SW) 2499 37 (2,S) 2226
Windows Max 9 (1,S) 2517 9 (1,SW) 2504 37 (2,S) 2234
Walls Min. 56 (3,S) 217 56 (3,S) 216 56 (3,S) 186
Windows Min. 56 (3,S) 212 56 (3,S) 210 52 (3,S) 178
Roof 4635 4582 4567
Building 1269 1241 1107
Summer
Walls Max 2 (1,N) 2914 2 (1,N) 2914 2 (1,N) 2914
Windows Max 39 (2,W) 2243 9 (1,S) 2140 38 (2,W) 1975
Walls Min. 52 (3,S) 222 52 (3,S) 221 52 (3,S) 199
Windows Min. 52 (3,S) 225 52 (3,S) 224 52 (3,S) 196
Roof 5511 5469 5451
Building 1297 1265 1195
Fall
Walls Max 9 (1,S) 2860 37 (2,S) 2837 37 (2,S) 2118
Windows Max 9 (1,S) 2871 36 (2,S) 2841 58 (3,S) 2216
Walls Min. 55 (3, S) 264 55 (3, S) 262 52 (3,S) 164
Windows Min. 22 (2,N) 196 22 (2,N) 193 52 (3,S) 143
Roof 3452 3415 3294
Building 1170 1152 872
Annual
Walls Max 9 (1,S) 2617 37 (2,S) 2590 37 (2,S) 1999
Windows Max 9 (1,S) 2626 36 (2,S) 2597 58 (3,S) 2040
Walls Min. 55 (3, S) 275 55 (3, S) 273 52 (3,S) 172
Windows Min. 22 (2,N) 229 22 (2,N) 225 52 (3,S) 160
Roof 4082 4037 3956
Building 1217 1191 973

54

on the second floor with a primarily west orientation. It is important to note, due to the
skewed orientation of the building, this zone also receives a portion of direct sunlight
from the south as well. The zones receiving the least amount of radiation throughout the
year are primarily located with the alcove at the south end of the building, with the
exception of the winter months when the Sun’s rays are able to reach deep into this
recessed portion of the building. In the winter months, Zones 22 and 43 receive the least
radiation due to their northern exposures and decreased daylight during this time of year.
In an unobstructed context, Zone 37, with a southern exposure and location near the
western corner of the second floor, receives the largest amount of sunlight, due to being
the least overshadowed of all zones at the southern end of the building, during the winter
months.
The lowest overall building averages occur in winter due to the lower solar
elevation of the Sun and minimal sunlight hours. Although the Sun path is equivalent in
both spring and fall, variations in weather and atmospheric conditions have a direct effect
on the amount of direct and diffuse radiation received and as a consequence, the total
radiation varies. The overall buildings radiation values are highest in the summer. The
terrace on the northern portion of the building receives the highest amount of radiation
resulting from the Sun’s high solar elevation and increased direct sunlight. Horizontal
surfaces, such as those found on the terraces are most susceptible to the highest heat gains
because they experience the most direct sunlight. The terrace at the North end of the
building has minimal shading which may have been overlooked due to the fact it is
located on the northern side of the building. On the south side, the terrace is sheltered by

55

a large roof overhang and also benefits from being setback into an alcove portion of the
building. Zones within the alcove on the southwestern exposure receive the lowest
amount of radiation because they are shaded by the roof’s structure and set back from the
majority of the southern façade.
4.2 Phase 02 Results
The proximity and height of neighboring buildings can impact the solar insolation
received by a building’s envelope. Although Hubbard Hall and the Town and Gown
building are approximately equivalent in height compared to Lewis Hall, their close
proximity directly blocks sunlight in the evening hours. These two buildings have the
greatest effect on the western exposure at all times of the year.
Figure 16 shows the values for Phase 02 aggregated by orientation. The southern
exposure continues to receive the highest amount of radiation during the winter months,
while the western exposure has the highest values of the four exposures during summer.
The values at the roof continue to be the highest during all seasons except summer, where
the southern exposure, still receives a large amount of sunlight despite obstruction from
the Musick (Law) Building.
Figure 17 shows the impacts on each orientation by percent that the addition of
site context has on solar radiation values for each season. A review of the shading
heliodon (Appendix B) reveals that Popovich and Musick Halls have the largest
overshadowing effects in the winter months, reducing total radiation values on the eastern
and southern exposures by 2 and 4 percent, respectively. Musick Hall is fairly large in

terms of height but its relatively small impact on zones
mostly to the large distance between Lewis
and Gown structures have the largest effect on
along the western exposure. The height and c
large portion of the Sun rays
much in the winter due to the western
Sun’s shortened path during this season.
the north to northeast, are either too far away or too small to have significant impact
Figure 16 Phase 02 results by orientation
relatively small impact on zones with a southern exposure
distance between Lewis Hall and itself. Hubbard Hall and the Town
have the largest effect on solar access to Lewis Hall, most notably
stern exposure. The height and close proximity of these building blocks
Sun rays, especially in the summer months. The impact is not felt as
in the winter due to the western exposures receiving minimal sunlight due to
s shortened path during this season. The locations of the remaining structures,
ast, are either too far away or too small to have significant impact

56

exposure is due
Hubbard Hall and the Town
all, most notably
these building blocks a
months. The impact is not felt as
receiving minimal sunlight due to the
The locations of the remaining structures, from
ast, are either too far away or too small to have significant impacts.

Figure 18 shows the results
receive the highest amounts of radiation. Compared to Phase 01
can be noted. This is largely due to the fact that when data is aggregated by floor
approach takes into account all expos
averaging out major impacts with exposures that experienced little or no impact.
Zone 16, which is the w
significantly impacted zone following t
The taller buildings, which cast longer shadows, are also responsible for a reduction in
total radiation values. The zones receiving the
Figure 17 Phase 02 impacts
shows the results by floor which indicate that the first floor continues to
receive the highest amounts of radiation. Compared to Phase 01, no significant impact
can be noted. This is largely due to the fact that when data is aggregated by floor
takes into account all exposures circumnavigating the building, and therefore
averaging out major impacts with exposures that experienced little or no impact.
which is the western entrance to the first floor corridor, is the most
zone following the addition of this existing site context
aller buildings, which cast longer shadows, are also responsible for a reduction in
total radiation values. The zones receiving the least radiation input remain

57

indicate that the first floor continues to
no significant impact
can be noted. This is largely due to the fact that when data is aggregated by floor, this
circumnavigating the building, and therefore
averaging out major impacts with exposures that experienced little or no impact.
orridor, is the most
site context (Table 4).
aller buildings, which cast longer shadows, are also responsible for a reduction in
remained unchanged

throughout all seasons due to their primarily north
changed the least because they are location in the heavily shaded portion directly in the
recessed area on the southwestern
windows in Zone 39 on the second floor
highest total radiation in the summer months, but this value
once Hubbard Hall was added to the analysis.
However, the impact o
surrounding buildings was
received from the roof was
other zones and exposure

Figure 18 Phase 02 results by floor
hroughout all seasons due to their primarily north-facing orientations. Zones 52 and 55
because they are location in the heavily shaded portion directly in the
outhwestern exposure (Appendices B, C). In the previous phase
39 on the second floor of the northwestern exposure receiv
highest total radiation in the summer months, but this value was reduced
was added to the analysis.
impact on the roof and overall solar radiation values
surrounding buildings was relatively minor. Across all four seasons, the solar radiation
was reduced by approximately 4.5 percent and the
other zones and exposures were less than 1 percent in most instances (Figure 17)

58

facing orientations. Zones 52 and 55
because they are location in the heavily shaded portion directly in the
previous phase, the
received the
by 12 percent
n the roof and overall solar radiation values of adding the
solar radiation
and the changes to
less than 1 percent in most instances (Figure 17).

59

4.3 Phase 03 Results
As buildings become taller and density increases, the daylight reaching surrounding
buildings is diminished. The height and close proximity of the new Verna and Peter
Dauterive Hall will greatly impact the amount of total solar insolation, most notably for
the zones with south and western orientations
Figure 19 shows the results of Phase 03 with zones aggregated by orientation. The
highest radiation is still found on the roof surface, with the exception of the spring
season, which is now dominated by the eastern exposure. The values for the southern
exposure remain relatively consistent throughout the year, with the exception of the
winter season, where the new building will obstruct the majority of the Sun’s rays
coming in at low elevations. It is also interesting to note that the summer values for the
southern are the second highest of the four orientations, whereas in the two previous
phases, these values were the lowest. The northern exposure values continue to be the
lowest throughout the year, but almost equal the southern exposure values in the summer
season.
Figure 20 shows the percent decrease of solar radiation for each season by
grouped by orientation. The southern exposure is predicted to feel the largest impact,
especially in the winter months when there is a decrease of over 60 percent. The western
exposure also will experience a large drop in solar energy during the winter season. There
is little to no change for the northern exposure as well as the roof, with the exception of
the winter months. The shadow heliodons in the appendices show how the new building
will cast a large shadow over most of Lewis Hall during the winter months. Because of

Figure 19
the skewed orientation of
eastern exposure as well, most notable in fall and winter, where there is a 12% decrease
of solar heating energy.
The results aggregated by floor
receives the most radiation
During the winter and fall
compared to other floors.
throughout all four seasons
Zones on the third floor at the southern end of the building receive the least
radiation in all four season
under the roof overhang within the alcove was already low in previous phases. The new
construction project diminishes any chance of sunlight of reaching those zones at all
Figure 19 Phase 03 results by orientation
the skewed orientation of the University Park campus, the impact is also felt at the
eastern exposure as well, most notable in fall and winter, where there is a 12% decrease
The results aggregated by floor are shown in Figure 21. The first floor no longer
radiation in all seasons; it only does so during the spring and summer.
During the winter and fall, the second floor now receives notably more solar radiation as
compared to other floors. The third floor continues to receive the least radiation
seasons, with roughly similar values to the first floor during win
Zones on the third floor at the southern end of the building receive the least
radiation in all four seasons in Phase 03. The amount of sunlight reaching the third floor
under the roof overhang within the alcove was already low in previous phases. The new
construction project diminishes any chance of sunlight of reaching those zones at all

60

campus, the impact is also felt at the
eastern exposure as well, most notable in fall and winter, where there is a 12% decrease
21. The first floor no longer
all seasons; it only does so during the spring and summer.
olar radiation as
The third floor continues to receive the least radiation
the first floor during winter.
Zones on the third floor at the southern end of the building receive the least
s in Phase 03. The amount of sunlight reaching the third floor
under the roof overhang within the alcove was already low in previous phases. The new
construction project diminishes any chance of sunlight of reaching those zones at all

times of the year, with Zone 52 now receiving the less sunlight than other parts of the
building. The highest values still remain at Zone 2 during the summer months and at
Figure 20 Phase 03 Impacts
Figure 21 Phase 03 results by floor
times of the year, with Zone 52 now receiving the less sunlight than other parts of the
building. The highest values still remain at Zone 2 during the summer months and at

61

times of the year, with Zone 52 now receiving the less sunlight than other parts of the
building. The highest values still remain at Zone 2 during the summer months and at

62

zones adjacent to it, which share a large portion of horizontal surface fully exposed to
high amounts of direct sunlight. The windows at Zone 38 receive the highest amounts of
sunlight during the summer months despite their southern exposure; their location near
the corner of the building allows some sunlight to sneak by the new construction. This is
true for other areas as well: Zones 29, 37 and 58, for example, are located near corners
creating exposures from several directions throughout the year.
The impact of the new building on Lewis Hall is the greatest during the winter
months when the sunlight hours are fewer and the Sun’s position is low in the sky. Zones
10, 11, and 13 all have southern exposures and will receive up to 78% less solar radiation
due to direct blockage of sunlight when the new building is finished. The overall impacts
by zone are summarized in Appendix D. The summer months are the least impacted
during this phase, due to the Sun’s high altitude and direct rays reaching over the new
building to the lower floors of Lewis Hall. The most impacted zones, on the third floor,
are shown to have radiation values reduced by up to 25%. The maximum impact in the
spring is 43% for Zone 43 compared to 63% in the fall, for example. The direct sunlight
is greater and makes up the majority of solar energy in the fall season. This direct
sunlight will be blocked directly by the construction of the new building.
As discussed earlier, the roof can be thought of as a solar energy receiver that
heats the building as a whole. Assuming constant thermal properties throughout the roof
structure, solar radiation received at the roof surface will be dispersed as heat throughout
the building, but will be felt most noticeably on the fourth floor, a potentially useable
space that was not evaluated in this project, as well as on the third floor. Similar results

were predicted for the third floor
during the winter, compared to just a
reduction over the course of the
Figure 22 Variations in roof solar radiation predictions over the three phases

for the third floor which will experience a 10% reduction in Phase03
compared to just a 1% reduction in the summer season, and a 3%
rse of the whole year (Figure 22).
Variations in roof solar radiation predictions over the three phases

63
10% reduction in Phase03
season, and a 3%

Variations in roof solar radiation predictions over the three phases

64

CHAPTER FIVE: DISCUSSION AND CONCLUSIONS
The primary purpose of HVAC systems is to compensate the indoor environment by
either increasing or decreasing air temperature to an acceptable level. Current systems in
place monitor the occupancy of the indoor environment as an input to HVAC demand
controlled systems. This research case study has laid out the need to include changes in
the outdoor environment which are dynamic and yet predictable as well most focus is on
buildings that have not been constructed in the early design stage. In addition, the results
can be used to identify target areas of a building that may be considered for temporary or
permanent energy efficient design modification. This may be cost beneficial in
comparison to making entire building changes. Improving and evaluating existing
buildings will prove beneficial for energy consumption. The results of this research
indicate a need to evaluate spatial context of a buildings location and how this impacts
building energy management systems. This study proves that heat gains may be
predictable at a thermal zone level. By evaluating heat gains and correlating with
occupancy scheduling, heating cooling demands can be controlled at increased precision,
saving energy and money.
In Mediterranean climates, like that found in Los Angles, cooling is the primary
concern and makes up the majority of a building’s energy usage. Winters are relatively
mild, yet some energy usage also can be attributed to heating during cooler months and
especially early in the morning and late in the evening.
The addition of Verna and Peter Dauterive Hall will have large impact on the
solar radiation budget of Lewis Hall. This may be beneficial in terms of the cooling of the

65

building during the summer and considering the year as a whole. However, the building
will not receive the benefits of low sun and this may increase the demand for heating
during the cool season. The shadow cast from the new building and the effect this has in
terms of blocking direct sunlight also decreases the amount of natural sunlight that
reaches interior spaces of the building. This will, in turn, increase the demand on lighting
systems.
The current design and layout of the building is good and there is evidence that
solar heat gains were considered in the design process. The skewed east of north
orientation welcomes the morning sunlight without allowing in large amounts of direct
sunlight during the day. The north façade has the most fenestrations (i.e. openings in
walls for windows and doors), but receives the least amount of direct sunlight, thereby
minimizing heat gain through windows and other openings. Large windows located
throughout the north façade also allow diffuse sunlight into the building which benefits
the conference center located on the third floor and the entrance alcove. Similarly, the
spaces located within the alcove on the southern façade benefit from being located in the
recessed portion of the building under the overhang of the roof because this helps reduce
heat gain during the spring and fall months, when it was shown that the Sun’s rays have
the greatest effect on the southern façade. This arrangement would also be beneficial
during the winter months by allowing a small amount of heat gain during the coldest
season. However, the new building will eliminate most, if not all, of these beneficial
outcomes. Finally, the terrace at the northern end of the building is not shaded and

66

receives large amounts of solar energy, especially during the summer months, leading to
heat gains located in the zones directly below the terrace.
5.1 Implementation and Integration
Results from Phase 03 of this study can be used to anticipate changes in heat gain at
locations throughout the building once construction of the new Verna and Peter Dautrive
Hall is completed. Radiation values can be used to calculate anticipated heat gain at a
thermal zone level at different times throughout the year. Quicker response to
temperature changes has been proven to save energy (e.g. Tachweli, Refai, and Fagan
2007). In the summer when HVAC is primarily used to cool the indoor air, understanding
which thermal zones may heat up more quickly may prove to be beneficial with the
integration of current BLEMS research and the various detailed predictions laid out in
this case study.
5.2 Solar Radiation Model Validation
The Ecotect software suite uses the ‘BS ISO 15469-1997 Spatial distribution of Daylight
– International Commission on Illumination (CIE) Standard Overcast Sky and Clear Sky’
for its illumination distribution model (Autodesk 2010). The Ecotect analysis then uses
these radiance values, calculated using the above standard methods, as the model for a
detailed radiant-exchange analysis. All the solar position and solar radiation calculations
conform with the standards outlined in ‘CIBSE TM33 (2006) Tests for Software
Verification and Accreditation’ (Autodesk 2010). Solar access and rights-to-light
calculations conform to the building research establishment site planning handbook.

67

Vangimall et al. (2011) compared the accuracy of Ecotect estimates with actual field
measurements, and concluded that Ecotect overestimated the illuminance levels by
approximately 15% in the majority of cases. However, the study acknowledges that time
and date inputs were lacking in the model for the weather data that was used. The newest
version of Ecotect allows times and dates to be manipulated to account for both local and
global conditions, therefore solving this problem.
The Lewis Hall case study evaluated here used both of these factors as inputs into
the model. The close proximity of the weather station to the building site provides
additional confidence that the model would have performed better in the work at hand
than was the case in the study by Vangimall et al. (2011).

5.3 Study Limitations
Due to the limitations of the Ecotect software, trees and external foliage were not
included in this study model. The majority of the tress and landscaping that could have a
significant impact on the results presented in this thesis are located on the north side of
the building along the pathway, thereby minimizing this possibility because the northern
exposure has the least sunlight. The small bushes located directly adjacent to the building
would also have negligible effects on the solar radiation and/or overshadowing values.
However, the landscaping might be considered in future studies exploring ways to reduce
energy costs given that one study, by Santamouris (2011), showed that strategically
placed landscaping can reduce building energy costs by up to 10%.

68

5.4 Opportunities for Future Research
The simulations performed for this thesis could be taken further by including material
properties and insulation and thermal resistance of insulation and the reflectivity and
efficiency of the windows. With this information, detailed analysis can be conducted to
determine the heat flow from the outside environment to the interior spaces.
This study has focused on radiation values and the heat energy received from the
Sun which may have a large impact on building energy systems. The impacts of natural
sunlight or illuminance values on the building’s lighting systems were not considered.
With advances in light systems and energy efficient lighting, lighting has a relatively low
impact on energy costs compared to HVAC systems. However, illuminance values
should not be ignored, because natural sunlight plays an important role in the behavior
and satisfaction of a building’s occupants. In Phase 03 of this study, the construction of
the Verna and Peter Dauterive Hall was shown to have a large impact on the predicted
solar radiation values.
Direct and diffuse radiation values can also be considered separately by the
Ecotect software. Direct radiation can be shielded using either interior or exterior window
shading devices. Windows that were predicted to receive the highest amount of radiation
should be considered as high priority candidates for window treatments.
The temporal aspect of this study explored seasonal changes through four
different compilations of average daily values. The Ecotect software also allows analysis
at hourly increments and this would allow the comparison and analysis of how a building
is heated throughout the day by the Sun. Systems might be adjusted to compensate for

69

higher temperatures in spaces with eastern exposures in the morning and spaces with
western exposures late in the afternoon following this kind of detailed analysis for
example.
With the information gained from this study, temporary and permanent building
designs may be taken into consideration. Non-permanent changes such as the addition of
insulated window coverings may be considered for zones receiving high amounts of solar
radiation. Permanent considerations would include the addition of glazing or film to
windows which have been proven to transmit a high amount of radiation during the
warmer summer months. Increasing thermal mass or improving insulation in areas
susceptible to high radiation would also be desirable.
There have been many studies that have focused on improving energy efficiency in
buildings. Engineers and researchers have developed complex methods to improve
energy efficiency, but the buildings are often managed by non- specialized technicians
who need understandable and cost-effective methods to achieve the desired results in
their buildings. The results of this study can be exported directly from the Ecotect model
into other Building energy management programs such as Green Building Studio or
Energy Plus. The solar radiation values predicted in this study are directly proportional to
heat gain; however, further evaluation of exact heat gain calculations and resulting
energy flows within the building may be helpful for further understanding of the solar
heat flux and its implications.
This investigation has demonstrated the temporal variability of solar radiation
impacts on a building surface as well as how these impacts change based on the context

70

surrounding a building. In urban planning it is ideal to examine how future developments
impact the solar access to existing buildings. As building energy management systems
become more sophisticated, the acknowledgment of the dynamic exterior environment
should be considered for improved energy efficiency. The effects of solar radiation will
impact a building throughout its existence and should be evaluated at various levels, not
just during the design phase. This study has proven how the effects of solar radiation can
be considered at a more intricate level after the building has been occupied. Future
sustainable building designs need to have an intimate connection with their location as
well as the natural environment. As urban density increases the influences from natural
sources, such as the Sun, decreases and buildings will need to compensate for this impact.
The need to find a balance between these two phenomena will be beneficial for future
generations.

71

REFERENCES

Agarwal, Y., B. Balaji, R. Gupta, J. Lyles, M. Wei and T. Weng .2010. Occupancy-
driven energy management for smart building automation. In Proceedings of the
ACM Workshop On Embedded Sensing Systems For Energy-Efficiency In
Buildings (BuildSys '10), Zurich, Switzerland, 1-6.

Autodesk. 2013a. Autodesk Ecotect Analysis. Autodesk, Inc.
http://usa.autodesk.com/ecotect-analysis (accessed 30 June 2013).

Autodesk. 2013b. Validation of Ecotect Analysis. Autodesk, Inc.
http://usa.autodesk.com/adsk/servlet/ps/dl/item?siteID=123112&id=14576143&li
nkID=13734494 (accessed 13 August 2013).

American Society of Heating, Refrigerating and Air-Conditioning Engineers
(ASHRAE). 2005. Fundementals Handbook. Atlanta ,GA, ASHRAE.

Bourgeois, D., C. Reinhart and I. Macdonald. 2006. Adding advanced behavioral models
in whole building energy simulation: A study on the total energy impact of
manual and automated lighting control. Energy and Buildings 38: 814-823.

Brandemuehl, M. J. and J. E. Braun. 1999. Impact of demand-controlled and economizer
ventilation strategies on energy use in buildings. ASHRAE Transactions 105(2):
1-11.

Campbell, J. B. and R. H. Wayne. 2011. Introduction to Remote Sensing. New York:
Guilford Press.

Dean, J., A. Kandt, K. Burman, L. Lisell and C. Helm. 2009. Analysis of Web-Based
Solar Photovoltaic Mapping Tools. Golden, CO: National Renewable Energy
Laboratory.

Esri. 2013. An Overview of Solar Radiation Tools. Esri.
http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=An_overview_
of_the_Solar_Radiation_tools (accessed 30 January 2013).

Erickson, V. L. and A. E. Cerpa. 2010. Occupancy based demand response HVAC
control strategy. In Proceedings of the Second ACM Workshop on Embedded
Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland :7-12.

Fu, P. and P. M. Rich. 2000. The Solar Analyst 1.0 Users Manual. Lawrence, KS, H. E.
M. Institute.

72

GRASS (Geographic Resources Analysis Support System).2013. General Information.
GRASS. http://grass.osgeo.org/documentation/general-overview (accessed 20
January 2013).

Hofierka, J. and M. Zlocha. 2012. 3-D solar radiation model for 3-D city models.
Transactions in GIS 16: 681-690.

Hofman, H. 1980. Energy crisis-schools to the rescue again. School Science and
Mathematics 80: 467-478.

Jazizadeh, F., G. Kavulya, J.-Y. Kwak, B. Bercerik-Gerber, M. Tambe and W. Wood.
2012. Human-Building Interaction for Energy Conservation in Office Buildings.
In Construction Research Congress, 1830-1839. West Lafayette, IN: American
Society of Civil Engineers.
Kelso, J. D. 2011. Buildings Energy Data Book. U.S. Department of Environment.
http://buildingsdatabook.eren.doe.gov/ (accessed 01 September 2012).

Klein, L., F. Jazizadeh, G. Kavulya and B. Becerik-Gerber. 2011. Towards Optimization
of Building Energy And Occupant Comfort Using Multi-agent Simulation. In
Proceedings of the Twenty-eighth International Symposium on Automation and
Robotics in Construction, Mining and Petroleum, 251-256.

Li, N., G. Calis, and B. Becerik-Gerber. 2012.Measuring and monitoring occupancy with
an RFID based system for demand-driven HVAC operations. Automation in
Construction 24: 88-99.

Lo, L. J. and A. Novoselac. 2010. Localized air-conditioning with occupancy control in
an open office. Energy and Buildings 42:1120-1128.

Maktav, D., F. S. Erbek, and C. Jurgens. 2005. Remote sensing of urban areas.
International Journal of Remote Sensing, 26: 655-659.

Marsh, D. A. J. 2007. Why do shading calculations take so long? Natural Frequency
http://naturalfrequency.com/articles/shadingcalculations (accessed 30 July 2013)

Muneer, T. 2004. Solar Radiation and Daylight Models. Burlington, MA, Elsevier
Butterworth Heinemann.

NOAA (National Oceangraphic Atmospheric Administration). 2013. The Earth's Orbit.
NOAA. http://www.srh.noaa.gov/images/abq/cli/features/EarthOrbit.png
(accessed 30 September 2013).

73

Ogasawara, S., H. Taniguchi and C. Sukehira. 1979. Effect of energy conservation by
controlled ventilation: Case study of a department store. Energy and Buildings 2:
3-8.

Pavlovas, V. 2004. Demand controlled ventilation: A case study for existing Swedish
multifamily buildings. Energy and Buildings 36: 1029-1034.

Rossler, G. 2012. Behavior-based Control of Building Energy Systems. Los Angeles,
CA, University of Southern California Unpublished PowerPoint Presentation.

Santamouris, M. 2001. Thermal balance in the urban environment. In Energy and
Climate in the Built Urban Environment. ed. M. Santamouris. New York, NY:
Routledge: 39-45.

Stein, B., J. S. Reynolds, W. T. Grondzik, and A. G. Kwok. 2006. Mechanical and
Electrical Equipment for Buildings. Hoboken, NJ: John Wiley and Sons.

Strømann-Andersen, J. and P. A. Sattrup. 2011. The urban canyon and building energy
use: Urban density versus daylight and passive solar gains. Energy and Buildings,
43: 2011-2020.

Sun, Z., S. Wang and Z. Ma .2011 In-situ implementation and validation of a CO2-based
adaptive demand-controlled ventilation strategy in a multi-zone office building.
Building and Environment, 46: 134-133.

Súri, M. and J. Hofierka. 2004. A new GIS-based solar radiation model and its
application to photovoltaic assessments. Transactions in GIS, 8: 175-190.

Tachwali, Y., H. Refai, and J. E. Fagan. 2007. Minimizing HVAC energy consumption
using a wireless sensor network. In Proceedings of the Thirty-third Annual
Conference of the IEEE Industrial electronics, Piscataway,NJ, USA: 439-444.

Facilities Management Services. 2012. Lewis Hall CAD Drawings. Los Angeles, CA,
University of Southern California Facilities Management Services

Vangimalla, P., S. Olbina, R. Issa, and J. Hinze. 2011. Validation of Autodesk ecotect
accuracy for thermal and daylighting simulations, In Proceedings of the Winter
2013 Simulation Conference, Phoenix, AZ: 3383-3384.

Yang, Z., N. Li, B. Becerik-Gerber, and M. Orosz. 2011. A multi-sensor based
occupancy estimation model for supporting demand driven HVAC operations. In
Proceedings of the Spring 2012 Simulation Conference, Orlando,FL: 49-56.

74

APPENDIX A: CHARTS AND FLOOR PLANS

Figure 23 Sunpath chart for Los Angeles, CA (UO 2007)

75

Figure 24 Lewis hall floor plan with BLEMS zones: First Floor

76

Figure 25 Lewis hall floor plan with BLEMS zones: Second Floor

77

Figure 26 Lewis hall floor plan with BLEMS zones: Third Floor

78

Figure 27 Three dimensional view of building envelope with orientations

79

APPENDIX B: GRAPHIC RESULTS
Figure 28 Phase 01 results

80

Figure 28 Phase 01 results (Continued)

81

Figure 28 Phase 01 results (Continued)

82

Figure 28 Phase 01 results (Continued)

83

Figure 29 Phase 02 results

84

Figure 28 Phase 02 results (Continued)

85

Figure 28 Phase 02 results (Continued)

86

Figure 28 Phase 02 results (Continued)

87

Figure 30 Phase 03 results

88

Figure 30 Phase 03 results (Continued)

89

Figure 30 Phase 03 results (Continued)

90

Figure 30 Phase 03 results (Continued)

91

Figure 31 Annual comparisons of all three phases

92

APPENDIX C- RESULT CHARTS BY BLEMS ZONE
Figure 32 Comparison of each phase by BLEMS zone

93

Figure 32 Comparison of each phases by BLEMS zone (Continued)

94

Figure 32 Comparison of each phases by BLEMS zone (Continued)

95

Figure 32 Comparison of each phases by BLEMS zone (Continued)

96

Figure 32 Comparison of each phases by BLEMS zone (Continued)

97

APPENDIX D: IMPACT CHARTS BY BLEMS ZONE

Figure 33 Comparison of total impact by BLEMS zone

98

Figure 33 Comparison of total impact by BLEMS zone (Continued)

99

Figure 33 Comparison of total impact by BLEMS zone (Continued)

100

Figure 33 Comparison of total impact by BLEMS zone (Continued)

101

Figure 33 Comparison of total impact by BLEMS zone (Continued)

Abstract (if available)

Abstract The Sun is the center of our galaxy and its patterns have been studied by civilizations since the beginning of time. Solar energy is a complex phenomenon that is the basis for life on Earth. Understanding the position of the Sun during the day is critical for evaluating how its energy impacts our daily lives. In an urban environment, the Sun’s energy can be considered as a service as well as a burden. Solar energy is beneficial when it can be harnessed using solar collectors for electric generation or when it contributes to heat energy with passive heat gains in the winter. However, solar energy can cause unwanted heat gains during warm summer months when buildings are trying to keep occupants cool. Solar radiation models used to evaluate favorable conditions and locations have traditionally only required two-dimensional data for evaluation of terrain and rooftops. However, in order to attempt a comprehensive assessment of solar radiation effects with a built environment, three-dimensional data must be used to evaluate vertical surfaces as well. The proposed research can be used to evaluate solar radiation variations at a temporal scale resulting from a building’s location as well as spatial variations resulting from changes in the urban landscape. The investigation is centered on an educational building, Lewis Hall, located on the University Park campus of the University of Southern California. The impacts of solar energy evident in the following research should be considered when evaluating and designing efficient building energy systems in the future.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Closed landfills to solar energy power plants: estimating the solar potential of closed landfills in California

PDF

Harvesting energy with solar panels and adaptive shading for building skins: A case study of an office building in Saudi Arabia

PDF

Calculating solar photovoltaic potential on residential rooftops in Kailua Kona, Hawaii

PDF

Developing a replicable approach for the creation of urban climatic maps for urban heat island analysis: a case study for the city of Los Angeles, California

PDF

Exploring the pernicious effects of redlining and discriminatory policies on an American city: a spatio-temporal case study of New York City

PDF

Modeling burn probability: a Maxent approach to estimating California's wildfire potential

PDF

Visualizing historic space through the integration of geographic information science in secondary school curriculums: a comparison of static versus dynamic methods

PDF

The effectiveness of enviro-materially actuated kinetic facades: evaluating the thermal performance of thermo-bimetal shading component geometries

PDF

Modeling patient access to point-of-care diagnostic resources in a healthcare small-world network in rural Isaan, Thailand

PDF

Landscape and building solar loads: development of a computer-based tool to aid in the design of landscape to reduce solar gain and energy consumption in low-rise residential buildings

PDF

Spatiotemporal analysis of the SLOSH and ADCIRC storm surge models: a case study of hurricane Ida

PDF

Impacts of vegetation management on wildfire severity: a study of the 2021 Caldor fire

PDF

Pushing the solar envelope: determining solar envelope generating principles for sites with existing buildings

PDF

Spatial delineation of market areas: a proposed approach

PDF

Evaluation and development of solar control screens: using daylight simulation to improve the performance of facade solar control screens

PDF

A comparison of address point and street geocoding techniques in a computer aided dispatch environment

PDF

Geographic object based image analysis for utility scale photovoltaic site suitability studies

PDF

Surface representations of rainfall at small extents: a study of rainfall mapping based on volunteered geographic information in Kona, Hawaii

PDF

Spatio-temporal analysis of Western snowy plover nesting at Vandenberg Air Force Base

PDF

Investigating bus route walkability: comparative case study in Orange County, California

Asset Metadata

Creator Laur, Amanda Kim (author)

Core Title Modeling the spatio-temporal variability of solar radiation on buildings: a case study of Lewis Hall

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 03/14/2014

Defense Date 01/24/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag building energy management,Ecotect,OAI-PMH Harvest,solar radiation,spatial science

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Wilson, John P. (committee chair), Becerik-Gerber, Burcin (committee member), Ruddell, Darren (committee member)

Creator Email aklaur01@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-370202

Unique identifier UC11295443

Identifier etd-LaurAmanda-2299.pdf (filename),usctheses-c3-370202 (legacy record id)

Legacy Identifier etd-LaurAmanda-2299.pdf

Dmrecord 370202

Document Type Thesis

Format application/pdf (imt)

Rights Laur, Amanda Kim

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA