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ctrl+z: exploring the effects of undoing retrofits to pre-war buildings in Los Angeles

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Content 1

ctrl+z:

Exploring the Effects of Undoing Retrofits to Pre-War Buildings in Los Angeles

by

Geoffrey S. Becker

A Thesis Presented to the
FACULTY OF THE USC SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA

In Partial Fulfillment of the
Requirements for the Degree
MASTER OF BUILDING SCIENCE

August 2015
2

Acknowledgements
Though my name appears alone on the cover page, this thesis is the result of the work and dedication of a
number of individuals. First and foremost, I owe my thesis committee my sincerest gratitude. Chair Kyle Konis
and committee members Anders Carlson, Douglas Noble, and John Lesak have proven invaluable in their
contributions, working to define the goals of this thesis, guide my methodology, and improve my writing. Without
them, there would be no ctrl+z.

The inspiration for this thesis comes, in large part, from my friends and colleagues in USC’s Master of Heritage
Conservation program. Though the MBS and MHC programs do not typically share courses, they have “adopted”
me as one of their own, encouraging my passion for historic buildings and improving our extensive existing
building stock. Their suggestions and input have been crucial to this thesis and my academic and professional
development.

My family has been a constant source of support, through all of my endeavors, and without them I would not
even be at USC to take on the challenge of writing a master’s thesis. Since I was young they have encouraged
me in my passion for the built environment and allowed me to explore the different options available to me. Their
support (emotional and financial), is the reason why I was able to complete this undertaking.

Finally, I share the success of this thesis with my MBS family. The students, alumni, faculty, and staff that make
up the Master of Building Science program have made my graduate school experience a positive and memorable
one. Whether it be providing help with a technical problem, listening as I vent frustrations, or just having fun, I
can always count on the MBS family to be there for me. I am so excited to see where our professional lives take
us, and look forward to future opportunities to work together.
3

Abstract
To achieve the 80% statewide greenhouse gas reduction target mandated by California Executive Order
S-3-05, the existing building stock must become 40% more energy efficient by 2030. Meeting the target for
existing buildings will require deep energy retrofits to the 465-million square meters of existing commercial
building stock in the next 15 years. While studies have shown that energy retrofits leading to reductions as
high as 28% are possible in a cost-effective manner, and that “deep” energy retrofits can achieve even
greater reduction, these recommendations are often poorly applicable to historic buildings for two reasons.
First, the recommendations nearly always promote incremental improvements to a sealed building
envelope and efficiency upgrades to mechanical HVAC and lighting systems. This approach overlooks the
possibility of meeting energy reduction targets simply by re-activating the passive strategies present in
many historic structures, which have largely been de-activated over time following past renovations which
introducing air conditioning and fluorescent lighting technologies. Second, many retrofit recommendations
would alter the character of historic structures to an extent that would be unacceptable to preservationists,
the public, and existing historic preservation laws.
Because a large portion of the existing commercial building stock in Downtown Los Angeles was built before
1950 and originally designed to rely on the local climate for most Indoor Environmental Quality (IEQ) needs,
an alternate approach to deep energy retrofits is needed which begins by examining the energy reductions
achievable through re-activation of these original passive strategies.
To address this need, a simulation-based framework was developed to compare annual energy and IEQ
outcomes from re-activation of multiple passive strategies. Detailed building information on LA’s Subway
Terminal Building (with observational analysis of ten additional pre-1950s buildings) was used as a basis
to develop the initial baseline model and identify original passive design strategies (e.g. exterior solar
control, daylighting, and natural ventilation). Energy and daylight simulations using the EnergyPlus and
Radiance engines are used to quantify annual performance outcomes from a parametric exploration of
retrofit combinations that replicate and improve upon the original passive design intent of the historic
building type. Compared to the baseline model, implementation of the best set of passive retrofits was found
to yield a reduction in Energy Use Intensity (EUI) of 29%. Due to the typical design conventions of this
building type, a parametric model was developed to extrapolate the potential of re-activating passive
strategies for additional historic buildings. Analysis of the metric thermal autonomy revealed that thermal
comfort conditions can be met in pre-1950s buildings for large periods (up to 67%) of occupied hours
utilizing only passive conditioning. Analysis of the metric daylight autonomy revealed that floor plates could
be effectively daylit for up to 61% of occupied hours, depending on solar orientation. The paper concludes
with discussion of implications and limitations of the simulation framework for urban scale evaluation of
passive performance in pre-1950s buildings.

4

Table of Contents

Acknowledgements ....................................................................................................................................... 2
Abstract ......................................................................................................................................................... 3
Table of Contents..........................................................................................................................................4
Table of Figures ............................................................................................................................................ 6
Table of Tables ............................................................................................................................................. 8

1 Introduction ............................................................................................................................................ 9
1.1 The Problem .................................................................................................................................. 9
1.2 Research Question ..................................................................................................................... 10
1.3 Importance .................................................................................................................................. 10
1.3.1 Climate Change is Real .......................................................................................................... 10
1.3.2 The Effects of Climate Change are Potentially Devastating ................................................... 14
1.3.3 Buildings Play a Big Role ........................................................................................................ 15
1.4 Terms .......................................................................................................................................... 15
1.5 Study Boundaries ........................................................................................................................ 16
1.6 Deliverables................................................................................................................................. 16
1.7 To Be Discussed ......................................................................................................................... 16
2 Research Background ......................................................................................................................... 17
2.1 Vernacular Architecture ............................................................................................................... 17
2.1.1 The Importance of Climate ...................................................................................................... 17
2.1.2 Strategies ................................................................................................................................ 19
2.2 Mechanical Air Conditioning ........................................................................................................ 23
2.2.1 History ..................................................................................................................................... 23
2.2.2 System Types .......................................................................................................................... 24
2.2.3 Implementation in Buildings .................................................................................................... 24
2.3 Building Retrofits ......................................................................................................................... 25
2.3.1 Building Lifecycle..................................................................................................................... 25
2.3.2 Intervention Types ................................................................................................................... 25
2.3.3 Performing an Energy Retrofit ................................................................................................. 26
3 Methodology ........................................................................................................................................ 32
3.1 Precedent Analysis ..................................................................................................................... 32
3.1.1 Multi-Building Assessment ...................................................................................................... 32
3.1.2 Subway Terminal Building Case Study ................................................................................... 36
3.2 Simulation .................................................................................................................................... 38
5

3.2.1 Approach ................................................................................................................................. 38
3.2.2 Simulation Software ................................................................................................................ 39
3.2.3 Model Geometry ...................................................................................................................... 39
3.2.4 Variables ................................................................................................................................. 41
3.2.5 Metrics ..................................................................................................................................... 48
3.3 Extrapolation ............................................................................................................................... 49
3.3.1 Zone to Building ...................................................................................................................... 50
3.3.2 Zone to Building Stock ............................................................................................................ 50
4 Results ................................................................................................................................................. 52
4.1 Summary of Results .................................................................................................................... 52
4.2 Granular Results ......................................................................................................................... 58
4.2.1 WWR-20 .................................................................................................................................. 59
4.2.2 WWR-30 .................................................................................................................................. 63
4.2.3 WWR-40 .................................................................................................................................. 68
4.2.4 WWR-50 .................................................................................................................................. 73
5 Discussion ............................................................................................................................................ 79
5.1 Summary ..................................................................................................................................... 79
5.2 The Benefits of Environmentally Responsive Construction ........................................................ 79
5.3 Discussion of Variables ............................................................................................................... 80
5.3.1 Mixed-mode Ventilation ........................................................................................................... 80
5.3.2 Photosensor-Controlled Lighting Systems .............................................................................. 83
5.4 Discussion of Limitations ............................................................................................................. 84
5.4.1 Idealized Circumstances ......................................................................................................... 84
5.4.2 Shading Devices ..................................................................................................................... 85
5.4.3 Thermal Mass .......................................................................................................................... 85
5.5 Deliverables................................................................................................................................. 86
5.5.1 Calculation Methodology ......................................................................................................... 86
6 Conclusion ........................................................................................................................................... 87
6.1 Los Angeles Climate Goals ......................................................................................................... 87
6.2 Environmentally Responsive Design .......................................................................................... 87
6.3 Future Work ................................................................................................................................. 87
6.3.1 Integration with GIS Data ........................................................................................................ 87
6.3.2 Shading and Thermal Mass .................................................................................................... 88
6.3.3 Integration with Required Seismic Retrofits ............................................................................ 89
6.3.4 Analysis of Financial Return .................................................................................................... 89
6.4 Final Remarks ............................................................................................................................. 89
7 References ........................................................................................................................................... 90
6

Table of Figures
Figure 1. Energy Consumption in US, showing significant consumption for buildings ................................. 9
Figure 2. Historic CO2 levels and natural fluctuation compared to modern-day increases ........................ 11
Figure 3: Increased drought without emissions reductions ......................................................................... 12
Figure 4: Projected increased occurrence of extreme heat events ............................................................ 13
Figure 5: Mountain snowfall in 2100 ........................................................................................................... 13
Figure 6: Land, population, and monetary effects of global 1 meter rise in sea level................................. 14
Figure 7: Energy Consumption by end use in US buildings ....................................................................... 15
Figure 8: Map illustrating the different California Climate Zones in the Los Angeles Area ........................ 18
Figure 9: 1929 map showing the tilted Spanish grid and Jeffersonian grid of Los Angeles ....................... 20
Figure 10: Spanish Mission in nearby San Gabriel illustrating high thermal mass and low WWR ............. 21
Figure 11: The Avila Adobe......................................................................................................................... 23
Figure 12: Effect of various ECMs on Willett Center .................................................................................. 28
Figure 13: Typical U-values for old and refurbished wall assemblies ......................................................... 29
Figure 14: Left- Engraved cornerstone; Right- view of STB from Pershing Square ................................... 36
Figure 15: Map showing the location of the Subway Terminal Building in downtown Los Angeles ........... 37
Figure 16: Photograph of original blueprints ............................................................................................... 37
Figure 17: View of shoebox model from northeast with WWR of 0.30 ....................................................... 40
Figure 18: Diagram illustrating change-over mixed-mode system .............................................................. 42
Figure 19: Types of natural ventilation available in Honeybee ................................................................... 43
Figure 20: Adaptive standard for naturally ventilated buildings .................................................................. 45
Figure 21: Maximum and minimum outdoor allowable temperatures ......................................................... 45
Figure 22: Natural ventilation, zone thresholds, and OpenStudio systems components ........................... 46
Figure 23: Cold weather natural ventilation schedule for the first week of January ................................... 46
Figure 24: Hot weather natural ventilation schedule for the first week of August ....................................... 47
Figure 25: Collection of components used to automatically rotate the model during simulation ................ 48
Figure 26: Potential energy reduction of ER model over BAU model ......................................................... 52
Figure 27: Average of actual savings for all orientations with a given WWR ............................................. 53
Figure 28: Cooling energy use in BAU model ............................................................................................. 54
Figure 29: Cooling energy use in BAU model ............................................................................................. 54
Figure 30: Heating energy use in BAU model............................................................................................. 55
Figure 31: Heating energy use in ER model ............................................................................................... 55
Figure 32: Lighting energy use in ER model ............................................................................................... 56
Figure 33: Thermal autonomy for sealed and naturally ventilated zones ................................................... 57
Figure 34: Percent of time comfortable in BAU model ................................................................................ 57
Figure 35: Percent of occupied hours comfortable in ER model ................................................................ 58
Figure 36: Daylight autonomy for 4 WWRs ................................................................................................. 58
Figure 37: Thermal Comfort and thermal autonomy for WWR-2045............................................................ 61
Figure 38: Thermal Comfort and thermal autonomy for WWR-20180 .......................................................... 62
Figure 39: Thermal Comfort and thermal autonomy for WWR-30135 .......................................................... 66
Figure 40: Thermal Comfort and thermal autonomy for WWR-30315 .......................................................... 67
Figure 41: Thermal Comfort and thermal autonomy for WWR-40135 .......................................................... 71
Figure 42: Thermal Comfort and thermal autonomy for WWR-400 ............................................................. 72
Figure 43: Thermal Comfort and thermal autonomy for WWR-50135 .......................................................... 76
Figure 44: Thermal Comfort and thermal autonomy for WWR-500 ............................................................. 77

7

Figure 45: Mechanical vs. Natural Conditioning ......................................................................................... 80
Figure 46: Energy consumption by end use, grouped as BAU and ER by WWR ...................................... 81
Figure 47: Cooling energy by WWR and orientation .................................................................................. 81
Figure 48: Sun path diagram showing sun position and outdoor temperature ........................................... 82
Figure 49: Wind rose for downtown Los Angeles ....................................................................................... 83
Figure 50: Sample of building outline data generated as part of the LAR-IAC2 Project ............................ 88

8

Table of Tables
Table 1: Selected Pre-War Buildings .......................................................................................................... 33
Table 2: Characteristics shared by BAU and ER models ........................................................................... 38
Table 3: Opaque Constructions .................................................................................................................. 40
Table 4: Glazing Assembly ......................................................................................................................... 41
Table 5: Loads and Schedules ................................................................................................................... 41
Table 6: Set points and set backs ............................................................................................................... 43
Table 7: Savings by facade length .............................................................................................................. 50
Table 8: Values for determining building stock energy reduction ............................................................... 51
Table 9: Energy consumed by pre-war commercial buildings in downtown Los Angeles .......................... 51
Table 10: The potential savings in percent for given WWRs and orientations ........................................... 53
Table 11: Energy consumption by end use in BAU0 and ER0 models in kBtu/ft
2
/yr .................................... 59
Table 12: Energy consumption by end use in BAU45 and ER45 models in kBtu/ft
2
/yr ................................. 59
Table 13: Energy consumption by end use in BAU90 and ER90 models in kBtu/ft
2
/yr ................................. 59
Table 14: Energy consumption by end use in BAU135 and ER135 models in kBtu/ft
2
/yr ............................... 59
Table 15: Energy consumption by end use in BAU180 and ER180 models in kBtu/ft
2
/yr ............................... 59
Table 16: Energy consumption by end use in BAU225 and ER225 models in kBtu/ft
2
/yr ............................... 59
Table 17: Energy consumption by end use in BAU270 and ER270 models in kBtu/ft
2
/yr ............................... 60
Table 18: Energy consumption by end use in BAU315 and ER315 models in kBtu/ft
2
/yr ............................... 60
Table 19: Energy consumption by end use in BAU0 and ER0 models in kBtu/ft
2
/yr .................................... 64
Table 20: Energy consumption by end use in BAU45 and ER45 models in kBtu/ft
2
/yr ................................. 64
Table 21: Energy consumption by end use in BAU90 and ER90 models in kBtu/ft
2
/yr ................................. 64
Table 22: Energy consumption by end use in BAU135 and ER135 models in kBtu/ft
2
/yr ............................... 64
Table 23: Energy consumption by end use in BAU180 and ER180 models in kBtu/ft
2
/yr ............................... 64
Table 24: Energy consumption by end use in BAU225 and ER225 models in kBtu/ft
2
/yr ............................... 64
Table 25: Energy consumption by end use in BAU270 and ER270 models in kBtu/ft
2
/yr ............................... 64
Table 26: Energy consumption by end use in BAU315 and ER315 models in kBtu/ft
2
/yr ............................... 64
Table 27: Energy consumption by end use in BAU0 and ER0 models in kBtu/ft
2
/yr .................................... 69
Table 28: Energy consumption by end use in BAU45 and ER45 models in kBtu/ft
2
/yr ................................. 69
Table 29: Energy consumption by end use in BAU90 and ER90 models in kBtu/ft
2
/yr ................................. 69
Table 30: Energy consumption by end use in BAU135 and ER135 models in kBtu/ft
2
/yr ............................... 69
Table 31: Energy consumption by end use in BAU180 and ER180 models in kBtu/ft
2
/yr ............................... 69
Table 32: Energy consumption by end use in BAU225 and ER225 models in kBtu/ft
2
/yr ............................... 69
Table 33: Energy consumption by end use in BAU270 and ER270 models in kBtu/ft
2
/yr ............................... 69
Table 34: Energy consumption by end use in BAU315 and ER315 models in kBtu/ft
2
/yr ............................... 69
Table 35: Energy consumption by end use in BAU0 and ER0 models in kBtu/ft
2
/yr .................................... 74
Table 36: Energy consumption by end use in BAU45 and ER45 models in kBtu/ft
2
/yr ................................. 74
Table 37: Energy consumption by end use in BAU90 and ER90 models in kBtu/ft
2
/yr ................................. 74
Table 38: Energy consumption by end use in BAU135 and ER135 models in kBtu/ft
2
/yr ............................... 74
Table 39: Energy consumption by end use in BAU180 and ER180 models in kBtu/ft
2
/yr ............................... 74
Table 40: Energy consumption by end use in BAU225 and ER225 models in kBtu/ft
2
/yr ............................... 74
Table 41: Energy consumption by end use in BAU270 and ER270 models in kBtu/ft
2
/yr ............................... 74
Table 42: Energy consumption by end use in BAU315 and ER315 models in kBtu/ft
2
/yr ............................... 74

9

1 Introduction
1.1 The Problem
One would be hard-pressed to read the literature on sustainability and building without coming across the
same assessment again and again: the built environment is a key contributor to climate change and other
environmental woes. Nearly half (47.6%) of all energy produced in the United States is consumed by
buildings making the building sector the largest single contributor to carbon emissions in the country with
44.6%. Much of that energy is related to the production of electricity, since three-quarters (74.9%) of the
nation’s electricity is used for the daily operation of buildings (2030 Inc. 2011). New design and construction
receives a lot of attention and is often heralded as a solution to these very serious problems. While there
is little doubt that the way new buildings are designed and built needs to adapt to meet the challenges of
our world, the existing building stock (currently estimated to be nearly 300 billion square feet) cannot be
ignored (2030 Inc. 2010). In California, it is estimated that existing buildings are responsible for one quarter
of the state’s greenhouse gas emissions (Efficiency and Renewable Energy Division California Energy
Commission 2013). To make significant positive change, it is essential to address this considerable part of
the market.

Figure 1. Energy Consumption in US, showing significant consumption for buildings
Some of Los Angeles’ city leaders see the need for this positive change in reducing greenhouse gas
emissions. The city has currently cut its emissions by 30% below 1990 levels with a goal of 35% by 2030
(Reyes 2014). Councilman Paul Koretz has proposed city goals for cutting greenhouse gas emissions to
80% below 1990 levels by 2050, warning that the consequences of not acting decisively against climate
change could be catastrophic. Koretz’s plan would have Los Angeles do more to reduce waste, utilize solar
energy (especially on rooftops), and retrofit existing buildings (Reyes 2014).
To meet aggressive climate and emissions goals, all existing buildings need to cut energy consumption by
an estimated 40% (Long 2011). In 2009, after the passage of AB 758, the California legislature created the
Comprehensive Energy Efficiency Program for Existing Buildings. This program directs the California
Energy Commission to create and execute a program to achieve cost-effective energy savings in existing
commercial and residential buildings across the state and is a crucial part of California’s energy efficiency
and environmental policy goals. The Commission hopes to encourage multiple pathways to energy
upgrades in order to allow consumers to take advantage of as many opportunities as possible (Efficiency
10

and Renewable Energy Division California Energy Commission 2013). But city officials and building owners
knowing that retrofits are necessary is not enough: clear guidance on the appropriate retrofit strategies is
necessary to effectively address issues of climate change. To make smart, meaningful decisions these
stake holders need reliable information about the entire lifecycle of a building and the effects of certain
energy and retrofit strategies. This guidance must be tailored to the individual requirements of each building
and unfortunately does not exist. The matter is made even more complicated when considering that
numerous buildings have already been retrofitted, though not necessarily in an energy-smart way. What
retrofit strategies are best? Should select old buildings be un-retrofitted? Is retrofitting enough to meet Los
Angeles’ goals? These are the unanswered questions that this thesis will address.
1.2 Research Question
What is the potential of reintroducing passive cooling, ventilating, and lighting strategies in pre-World War
II buildings in Los Angeles for addressing the city’s goal of reducing carbon emissions by 80% by 2050?
This research is built around the idea of embedded energy efficiency. In order to use pre-existing goals
and add a degree of relevance that could not be achieved as a stand-alone project, the climate objectives
of the city of Los Angeles are an important focal point. The underlying question of the research question is
how this reduction can be achieved at all, let alone with pre-war buildings. Since an energy consumption
reduction of 40% is necessary in every existing building to meet a carbon reduction goal of 80%, simulations
will be conducted to see if this reduction is possible for downtown Los Angeles’s approximately 16 million
square feet of pre-1950 commercial space (Long 2011).
1.3 Importance
1.3.1 Climate Change is Real
Ultimately this thesis will begin to determine how certain buildings can be modified to produce energy
savings. While savings for savings sake is not a bad thing, the importance of this research stems from
what City Councilman Koretz’s proposal (and many other proposals like it around the world) seeks to
prevent: global climate change.
Over the last century, scientists have measured both a rise in global temperatures and a rise in the
concentration of carbon dioxide in the atmosphere (IPCC 2007a; NASA 2014a). Deniers of human-caused
climate change are quick to point out that the earth has experienced natural variation in temperature
throughout its history. While this is true, the current rate of change is significantly faster than any other time
in recent history.
It has been proven that carbon dioxide can trap heat and rapid industrialization and increased consumption
has encouraged the release of billions of tons of CO2 annually (NASA 2014a; The World Bank 2014). This
helps to explain and further support some visible instances of the effects of climate change:
 Over the last 100 years, global sea levels have risen nearly seven inches, but the rate of change
has been nearly double that over the past ten years (Church and White 2006).
 Global land, air, and water temperatures have risen significantly since 1880, mostly over the past
forty years (NASA 2014).
 Ice melting causing shrinking ice sheets in Greenland and the Antarctic, declining sea ice in the
Artic Sea, and glacial retreat throughout the world (NASA 2014a).
 An increase in the number of extreme weather events throughout the United States with, in general,
more record highs and less record lows (NOAA 2014).
11

Figure 2. Historic CO2 levels and natural fluctuation compared to modern-day increases (NASA 2014a)
Contrary to what is often presented in the media, there is little disagreement among scientists when it comes
to the reality of climate change and its cause. Ninety-seven percent of climate scientists agree that the
measured trend of rising temperatures are very likely caused by human activity (NASA 2014b). Numerous
groups have issued statements supporting manmade climate change. The Intergovernmental Panel on
Climate Change (IPCC) summed up the position nicely, stating, “Most of the observed increase in global
average temperatures since the mid-20th century is very likely due to the observed increase in
anthropogenic greenhouse gas concentrations.” The term “very likely” is defined as greater than ninety
percent probability (IPCC 2007a). In other words, it is almost certain that we as a species are responsible
for the challenges we now must face as a planet.
1.3.1.1 Climate Change in the Southwest United States
The US Environmental Protection Agency (EPA) and the National Oceanic and Atmospheric
Administration’s (NOAA) National Climatic Data Center provide climate data detailing the changes
happening in the Southwest United States, California, and key cities in the region. The data at this scale
covers large areas of land with varying topography, ecosystems, and weather conditions, so the findings
are general predictions, not guarantees of what will happen in a specific location. The EPA has broken
down the effects of climate change in the Southwest into separate impact categories:
 Temperature. The region has experienced a temperature increase of approximately 1.5°F over the
past one-hundred years. Warming is expected to continue through the end of the next century,
adding up to another 8°F (Karl, Melillo, and Peterson 2009).
 Water Resources. Already-strained water resources will experience more constraint as
temperatures rise. This will be compounded by less spring rain and an increased frequency of
severe droughts. Fewer water resources will lessen groundwater recharge, rivers, lakes and
reservoirs, while increased competition could lead to conflict (IPCC 2007b; Karl, Melillo, and
Peterson 2009).
12

Figure 3: Increased drought without emissions reductions (Karl, Melillo, and Peterson 2009)
 Forests and Other Ecosystems. Increased temperatures coupled with less water and snow are
expected to increase the frequency and intensity of wildfires. Changing climates will create less
habitable environments for many species which must migrate to cooler or higher areas or face
extinction. Invasive species are likely to move in after the widespread migration or die-off of other
species, threatening biodiversity (Karl, Melillo, and Peterson 2009).
 Human Health. Air quality is likely to decrease further with warming temperatures. People suffering
from respiratory diseases are particularly at risk and the current 8,800 annual air pollutant-related
deaths are expected to increase along with the $1 billion plus in health related costs (Karl, Melillo,
and Peterson 2009).
 Energy. The ability to meet electrical demand as temperatures increase is a major concern.
Increased air-conditioner use coupled with growing populations and fewer hydroelectric resources
could threaten the distribution of electricity (Karl, Melillo, and Peterson 2009).
 Agriculture. There is potential for climate change to have some positive effects for select crops by
increasing productivity and minimizing freezing. However, many crops only grow within a range of
temperatures that will likely be exceed by the end of the century. As this region produces much of
the nation’s food, this will likely affect the food supply and prices (EPA 2013; Karl, Melillo, and
Peterson 2009).
1.3.1.2 Climate Change in Los Angeles and Environs
Although climate change is happening on a global scale, it is important to consider its implications at the
local level. C-Change.LA, a website focused on Los Angeles-centered studies for climate and the
environment, was published by Climate Resolve with research assistance from UCLA’s Institute of the
Environment and Sustainability to get a clearer picture of LA’s climatic future. The group’s unique study
entitled Mid-Century Warming in the Los Angeles Region used powerful computing tools to integrate up to
nineteen different global climate change models with the unique weather and topographic conditions of Los
Angeles. With this information, predictions for the climate variation were made for areas as small as a 1.2
mile square. To make the information more actionable for local officials, the period analyzed is 2041-2060;
time within the scope of long-term city planning (Climate Resolve 2012).
13

Each of the distinct neighborhoods in Los Angeles can be broken out and analyzed for future climate
patterns, but for all areas of the city, the findings are somewhat worrisome. Regardless of current efforts
to limit greenhouse gas emissions, the city will experience an average temperature increase of 4-5°F by
the middle of this century. Inland areas like the mountains and deserts will be affected the most, with a
likely warming of 4.5-5.5°F. Dense urban areas like downtown will likely see temperatures increase less,
but only slightly, with estimates placing mid-century temperatures 4-4.5°F higher. The ocean itself and
coastal cities will experience the smallest increase of a still-noticeable 3-4°F. All areas will experience more
“extreme heat days” (days exceeding 95°F). Most of LA’s population will experience triple the number of
these days that are currently normal, while some areas like the mountains will experience five to six time
more extreme heat days (Climate Resolve 2012).

Figure 4: Projected increased occurrence of extreme heat events (Climate Resolve 2012)
Los Angeles-area mountains will be hit considerably hard by rising temperatures. The Mid-Century
Warming study indicates that the region could lose up to 42% of its snowfall if emissions are left unchecked.
With immediate action for emissions reduction, the loss of snowfall could be limited to 31%. Snow on the
ground will also be affected, melting a full sixteen days faster in the spring by mid-century compared to
today, potentially altering important ecosystem processes (Climate Resolve 2012).

Figure 5: Left- Mountain snowfall in 2100 in conditions don't change; Right- Mountain snowfall in 2100 if
mitigation measures are effectively implemented (Climate Resolve 2012)
14

Sea level rise along Los Angeles’ coast is expected to match global predictions with an increase of 5 to 24
inches by 2050 and an increase of 17 to 66 inches by 2100. The coastal areas are already subject to
occasional flooding due to tides and storm surges and the frequency of these events could be greatly
increased.
Climate Resolve and UCLA are continuing to research the specific effects of climate change on Los
Angeles. Future studies will analyze precipitation, wildfire, Santa Ana winds, and Sierra Nevada snowpack.
Though the outlook is dire, it should be viewed as a call to action. While some effects of climate change
are now inevitable, mitigation strategies are an important part of trying to reduce atmospheric carbon
concentrations to 350ppm to avoid dangerous tipping points (350.org 2014). The hope that the worst can
still be avoided will hopefully encourage positive change.
1.3.2 The Effects of Climate Change are Potentially Devastating
The above changes would not necessarily merit alarm if they did not have real impacts on how the planet
functions and how we live our lives. Scientists point out that temperatures are rising, but unfortunately a
few degrees sounds trivial to a layperson. It is the effects that need to be made clearer, so that people can
understand how their lives might change.
The effects of rising temperatures are profound and far-reaching. Some of our greatest challenges and
losses will be due to rising seas as the polar ice caps melt and the water itself experiences thermal
expansion. Over the next century, sea levels could rise up to 36 inches. A rise of this magnitude would
result in every city on the United States’ east coast from Miami to Boston being flooded (The Nature
Conservancy 2014). Worldwide, an estimated 145 million people and $944 billion in property value would
be affected (Anthoff et al. 2006).

Figure 6: Land, population, and monetary effects of global 1 meter rise in sea level (Anthoff et al. 2006)
In addition to rising sea levels, coastal communities must face storms of greater length and intensity. With
high ocean temperatures, storms, hurricanes, and tropical storms are stronger and could cause more
property damage.
The human costs of rising temperatures are enormous. In 2006, California suffered major heatwave
resulting in almost 700 deaths, nearly 18,000 hospital visits, and $5.4 billion in costs (Natural Resource
Defense Council 2014). But according to a report released by the NRDC, that number will be dwarfed by
the death toll at century’s end. By 2099, 150,000 people are predicted to die due to extreme heat across
America, and at least 1,000 of those casualties will be in Los Angeles (Altman 2012).
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1.3.3 Buildings Play a Big Role
It bears repeating that buildings contribute to our changing climate in a very large way; more than any other
individual sector (2030 Inc. 2011). The Rocky Mountain Institute has found that buildings are responsible
for the consumption of more coal or gas than any other sectors in the United States. In fact, more than half
of the consumption of each of those resources is related to buildings. One of the main reasons for this is
the importance buildings have in our everyday lives; according to the EPA, Americans spend roughly 90%
of their time inside buildings (EPA 2011).
The US Department of Energy has broken down the energy end-uses in US buildings and three end uses
are responsible for half of the energy consumed in buildings: heating, cooling, and lighting. The energy
end use breakdown can vary considerably with building type. Space conditioning in residential buildings
consumes a greater portion of total energy consumed compared to commercial buildings, while commercial
buildings use twice as much energy for lighting (DOE 2012a).

Figure 7: Energy Consumption by end use in US buildings
Though not the single largest energy consumer in a commercial building, there are many opportunities for
efficiency improvement regarding space conditioning. Much of the energy spent in the conditioning of a
building (especially older buildings) is wasted. This is because these spaces are forced to utilize
mechanical conditioning (almost exclusively), but still suffer from leaks, inadequate insulation, and
improperly sized heating and cooling systems. It is estimated that 16% of the energy used to condition
spaces is lost to inefficiencies (Oak Ridge National Laboratory 2014). And it is not just energy use that is
the problem; conventional conditioning systems also use harmful refrigerants that accelerate global
warming while deteriorating the ozone layer, feeding the vicious cycle which only promotes more air
conditioner use with rising temperatures.
One of the challenges that must be addressed is that many buildings that currently depend on mechanical
heating and cooling were not originally designed to have such systems. Air conditioner use rose rapidly
after the Second World War and buildings that were built before then were retrofitted to use the new
technology. Addressing these buildings will be critical in mitigating climate change and its effects.
1.4 Terms
The following terms are from the fields of the building sciences and digital modeling and simulation. They
are used frequently throughout this research.
 energy conservation measure (ECM): any project or piece of technology whose purpose is to lower
energy consumption in a building
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 environmentally responsive ECMs: non-standard, research-driven ECMs which are typically, at
least in part, passive. Examples include introducing natural ventilation, high thermal mass with
night flush, daylighting, mixed mode ventilation, and shading devices. Often these ECMs cost more
upfront, but offer greater savings over the life of the building.
 business as usually ECMs: standard-driven ECMs currently found in typical retrofit projects.
Examples include a tightly sealed envelope, high(er) efficiency lighting systems, and high(er)
efficiency HVAC systems. These ECMs usually cost less up front.
 daylighting: the illumination of buildings by natural light. Numerous building components can assist
the success of daylighting such as glazing choice, light shelves, and surface colors.
 thermal comfort: ASHRAE has defined thermal comfort as “the condition of mind that expresses
satisfaction with the thermal environment and is assessed by subjective evaluation” (ASHRAE
Standard 55). In simpler terms, it is the state in which a building occupant is happy with the
temperature of a space, and is affected by temperature (of course), humidity, and air speed.
 thermal autonomy: a measure of how much a building’s architectural and ambient energy resources
are used to maintain thermal comfort.
 natural ventilation: the use of “natural forces of wind and buoyancy to deliver fresh air into buildings”
(Whole Building Design Guide).
 mixed mode ventilation: a hybrid of conventional HVAC technology with natural ventilation.
1.5 Study Boundaries
The primary focus of this thesis is the energy consumed by an existing building. This will be explored in
the context of different retrofit strategies that can be applied over the lifecycle of the structure. Existing
buildings and their energy consumption could relate to many other research topics which might be
mentioned, but not fully addressed by this thesis. These kind of topics include:
 How to increase the energy efficiency of registered Historic Cultural Monuments
 Indoor environmental quality improvements caused or restricted by any simulated retrofit strategies
 Improvements or detriments to building occupant productivity due to any simulated retrofit
strategies
 Improvements or detriments to building occupant well-being due to any simulated retrofit strategies
 Any savings (or increased consumption) of water due to any simulated retrofits strategies
 How to integrate environmentally responsive energy conservation measures with mandatory
seismic retrofits and upgrades
1.6 Deliverables
A framework has been developed to guide decision making for owners of certain pre-war buildings in
downtown Los Angeles. The framework indicates where retrofit dollars are best spent for lowering energy
consumption and meeting the carbon emissions targets of the City of Los Angeles. It can be applied to
similar commercial building and analyzes the effects of variables such as floor depth, window to wall ratio,
and shading devices. Results have been scaled to anticipate possible savings across the whole of Los
Angeles and the contribution of these efforts to meeting the city’s goals.
1.7 To Be Discussed
This chapter has introduced the problem facing existing buildings and mechanical space conditioning in
terms of climate change. The following chapters will explore the existing research about retrofits, vernacular
architecture, and different construction practices, as well as the methods used to conduct the research, the
gathered data, and interpretations of that data.

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2 Research Background
There are many factors to consider when exploring retrofits to historic buildings. First, it is important to
understand the strategies used by these structures for maintaining thermal and visual comfort. Many of
these strategies were developed over many generations to become the vernacular architecture of Los
Angeles. Second, it is important to understand how modern air conditioning has changed how buildings
are designed. Third, understanding retrofits themselves and how they are carried out helps to define the
best options for the historic buildings of Los Angeles.
2.1 Vernacular Architecture
Since man’s earliest structures, architecture has evolved in a continuing struggle to meet the shelter,
comfort, and aesthetic needs (or wants) of building occupants. These needs vary greatly from locale to
locale, resulting in the rich architectural history that can be seen around the world today. Through trial and
error, specific building materials and methods of construction have come to prominence, each especially
suited to the unique climatic conditions of the site. The resulting architecture is known as the local
vernacular.
2.1.1 The Importance of Climate
Climate is a driving force behind vernacular architecture, creating construction traditions across the globe
as varied as regional climates. Differences in temperature, humidity, precipitation, and wind have resulted
in architecture that is linked to its place, as well as to the culture which produced it. Cultures responding to
similar weather conditions often developed similar building types, even when separated by vast distances.
Without the benefit of mechanical conditioning systems, builders of vernacular structures utilized solar and
passive strategies to maintain the thermal comfort of their buildings. While many building strategies were
developed to respond to drastically different conditions around the world, only those that are appropriate to
the Los Angeles climate will be discussed.
Los Angeles is bounded by the Pacific Ocean to the south and west, and mountains to the north and east
(the Santa Monica Mountains and the San Gabriel Mountains, respectively) (Advameg, Inc. 2009). The city
counts canyons and valleys in its geography along with beaches and rivers, making the area incredibly
diverse both in terms of typography and climate. Due to its large size and varied geography, two separate
Köppen climate zones (Csa and Csb) and three California climate zones (6, 8, and 9) are used to classify
the regions that make up Los Angeles. Downtown Los Angeles (classified as Köppen Csa and CA Zone
9) lies about fourteen miles inland and is the focus of this research.
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Figure 8: Map illustrating the different California Climate Zones in the Los Angeles Area (California
Energy Commission 2014)
The classification of “Csa” designates a hot Mediterranean climate. These zones experience clear wet and
dry seasons (winter and summer, respectively) but yearly temperature swings are fairly moderate (Morris
2006). This is part of the reason why Los Angeles is famous for mild and pleasant weather.
Downtown LA’s hot, dry summers and moderate winters make the cooling season one of the biggest
concerns for building designers focused on occupant comfort. Summer temperatures typically peak in the
mid-eighties (°F), but temperatures above 100°F have been recorded (Morris 2006). The frequency of
these extreme heat days is likely to increase in the future due to climate change on a global and micro-
scale. Winters typically experience a significant period of cooling in the first half of November and fall as
low as the upper forties (°F) by December and January. However, the region’s famous Santa Ana Winds,
which are generally warm and dry, occur during these colder months and often alleviate some of the chill
experienced by residents. For other parts of the year, the predominant wind direction is west-southwest
with an average annual speed of 6.2 miles per hour (Morris 2006).
A major contributor to LA’s consistently fair weather is its year-round sunshine. Downtown averages 186
sunny days (0-30% cloud cover) per year and an estimated 73% of annual sunshine reaches the ground
(Current Results Nexus 2014). These conditions create generally favorable outdoor conditions, but can
also cause significant heat gain inside buildings.
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Historically, two groups have developed the vernacular architecture of southern California to respond to
these kinds of climatic conditions. The first group is the Native Americans of the Gabrielino-Tongva tribe,
which occupied much of the Los Angeles Basin. The second group consisted of the Spanish explorers and
missionaries which colonized the region in the sixteenth century (Oliver 1997). The following sections detail
the most significant strategies that apply to LA’s climate.
2.1.2 Strategies
Since thermal comfort is one of the reasons buildings were initially constructed in the first place, finding a
way to manage it was critical in the development of vernacular architecture. Many of the strategies that
became the norm for Los Angeles work together to create synergies that harness natural forces to make
comfortable spaces. Often the synergies go beyond thermal comfort and create conditions in which
buildings can make the best use of available daylight. The strategies discussed fall into the categories of
site, geometry, or materials and assemblies.
2.1.2.1 Site
One of the most important, yet simplest, strategies to be utilized in vernacular architecture is the proper
orientation of the structure on the site. The ideal orientation maximizes solar heat gain in the cooler winter
months while making it possible to block unwanted heat gain in the summer (Gromicko 2014). The longest
faces of the building should face north and south because these facades are the easiest to shade.
Extensive glazing can be implemented on the south side to allow solar heat gains from the low winter sun,
which is primarily in the southern sky. The north side should have less glazing to minimize winter heat loss
and shading to protect from the summer sun which rises and sets slightly north of east and west (Gromicko
2014).
Studies have shown that the orientation of a building has a significant effect on its energy consumption
(Andersson et al. 1985). This is because of the difference in solar gains when the primary glazed façade
is faced in a particular direct. In the heating season, gains from east or west facades are less than gains
from southern exposures, which then requires more energy to heat the building. In the summer however,
gains on the east or west facades are greater than gains on the south side causing overheating and
requiring energy to bring internal temperatures down to a comfortable level. The situation is similar for
buildings whose primary façade faces north, however studies have shown that a north orientation typically
has 6 to 17% higher loads than a south orientation for most of the country. Builders in Los Angeles, on the
other hand, must be mindful of overheating from the southern exposure, as winter gains may not be as
beneficial as summer gains are harmful (Andersson et al. 1985).
The sun is not the only factor to consider when a building is being oriented on its site. Wind, when properly
managed, plays a big role in inducing ventilation in a space and helping to maintain thermal comfort. Natural
wind forces can pull air through a building, preventing stagnation and causing occupants to perceive a
feeling of cooling. A building oriented toward prevailing winds is best suited to take advantage of these
forces (Autodesk, Inc. 2011).
Though there is no remaining evidence of the Gabrielino-Tongva tribe’s consideration of orientation, the
Spanish settlers’ concern for proper orientation is still a part of everyday life in downtown Los Angeles. El
Pueblo de Los Angeles was founded in 1781 under the Spanish Laws of the Indies: a collection of decrees,
mandates and laws governing how the Spanish settlers were to live in the New World and interact with its
inhabitants (“Laws of the Indies” 2014). Under these laws, cities were required to be laid out on a rectilinear
grid, oriented 45° from north. The reasoning behind this was two-fold: first, it was said that a small house
could get nearly equal amounts of sunlight on all sides when oriented this way, allowing most of the rooms
to be day lit at some point during the day. Second, this layout was meant to protect the pueblo’s busy
central plaza (around which all civilian Spanish settlements were to be built) from strong winds. Due to the
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geography of the Los Angeles River, settlers managed to establish the street grid of modern downtown at
36° from north (Waldie 2010).
Considerations of site and orientation remained largely unchanged for buildings constructed in downtown
Los Angeles in the first part of the twentieth century. The fact that much of downtown still uses the same
tilted street grid dictates much of the practices used.

Figure 9: 1929 map showing the tilted Spanish grid and Jeffersonian grid of Los Angeles (Leimert,
Donald, and Judge 1928)
2.1.2.2 Geometry
A building’s general shape is known to have an effect on its energy consumption. This is largely because
of the heat transfer that takes place at the surface of a building. Thus, the greater the surface area, the
greater the potential for the transfer of energy. Quite often relationship of building volume to surface area
is described as compactness (Straube 2012). While the compactness of a building can significantly affect
the energy performance of small buildings, it becomes less important for buildings of a larger scale (Gratia
and De Herde 2003; Straube 2012).
The cultures which inhabited the Los Angeles region typically embraced compactness with their buildings
and used simple shapes for building footprints. A circle plan creates the optimum volume to surface area
ratio (1) and was used quite often by the Gabrielino-Tongva people (Straube 2012; The Arboretum 2014).
The square, being near optimum, was used occasionally by the Spanish that followed, but there is a tradeoff
between compactness and other design considerations, namely daylight. A square runs the risk of creating
too deep a floor plate, and daylight would not be able to penetrate deep enough. The daylighting of spaces,
especially considering the thought put into their street grid, was a bigger priority to the Spanish settlers of
Los Angeles (Mundigo and Crouch 1977).
To better address their needs, the Spanish used different building geometry to both remain compact and
limit the depth of floor plates to maximize daylighting and natural ventilation strategies. Spanish buildings
in early California were open to promote natural ventilation and larger buildings had internal courtyards or
U-shaped footprints (Park 1999; Oliver 1997). These internal spaces acted both as sources of daylight and
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natural ventilation and as extensions of the living space to house essential functions such as cooking and
sanitation. For the rancheros, these spaces also acted as pens for livestock (Oliver 1997).
Orienting windows properly is very important for passive solar heating and for managing unwanted heat
gain, but the proportion of windows to be placed is just as important. This is because the proportion of
glazing on a building – commonly called window-wall ratio (WWR) – affects heat transfer between exterior
and interior environments and the transmittance of daylight into the space (Susorova et al. 2013). Optimal
window layout varies with climate zone and the orientation of each façade. The effects of WWR on energy
consumption are more prevalent in climate zones like that of Los Angeles, where energy savings are
greatest when north-oriented rooms have a high WWR and when south-oriented room have a low WWR.
The differences in WWR for each façade can be attributed to the need to find a balance between heat
gain/loss and daylighting. Large windows decrease the need for artificial light, but can also let in unwanted
solar heat gain (Susorova et al. 2013).
In the warmer regions of the Los Angeles Basin, Spanish buildings had a small WWR to maintain comfort
during the hot days. This strategy often depended on the thermal mass of the materials used, which is
discussed in the next section. In more moderate climates near the coast, large windows were used to
promote daylighting and ventilation. In both cases, however, shading was a critical consideration for
Spanish windows (Park 1999).

Figure 10: Spanish Mission in nearby San Gabriel illustrating high thermal mass and low WWR
(Mission San Gabriel Arcángel 2005)
Shading devices work hand-in-hand with building orientation and WWR to selectively allow solar heat gain
into the building. The options for shading devices are numerous and include fixed overhangs, exterior
sunscreen blades (horizontal or vertical), venetian blinds, shutters, and many more (Carletti, Sciurpi, and
Pierangioli 2014). An ideal shading installation is able to block direct solar radiation in the summer which
would cause the building to overheat, while in the winter would allow the space to be heated through direct
solar gains. Studies from ASHRAE have shown energy savings of up to 14 % with the addition of a shading
overhang (Newell and Newell 2010).
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Spanish shading devices varied with building type and the desired level of grandeur. Most buildings utilized
roof overhangs to shade openings in the envelope. Since most buildings were only one story, an overhang
such as this was often satisfactory. When more shading was needed, shutters were used on the exterior
to block solar gains while still allowing for some, albeit limited, natural ventilation (Park 1999). Larger homes
and more impressive public spaces almost always had verandas along one or more sides of the building.
This not only acted as protection from solar radiation without impeding natural ventilation, but also created
comfortable, shaded outdoor spaces (Oliver 1997).
The biggest single change in building geometry for Los Angeles structures was the change in size and
scale. Developments in manufacturing, distribution and construction allowed for the development of
projects that had both large footprints and very tall heights. Simple block shapes were still used the most
for building footprints, but other “alphabet” shapes were used to be able to introduce daylight and,
depending on the shape, natural ventilation. Common building shapes include “E”, “H”, “L”, “T”, and “U”.
Additionally, buildings with interior courtyards might be considered “D” or “O” (or “B” if there are multiple,
separate courtyards). WWR increased dramatically compared to the Spanish vernacular, with the average
at about 0.3. Internal shading devices were used by occupants to block excessive solar gains, however
existing documentation does not indicate extensive use of external shading devices.
2.1.2.3 Materials and Assemblies
Materials and how they are put together are a major contributor to the vernacular architectural styles found
around the world. Material availability greatly limited choices for early designers and encouraged the best
utilization of local resources. For many areas around the world, the best utilization of materials for
responding to climatic conditions called for the use of thermally massive assemblies. Thermal mass,
especially in areas with large diurnal temperature ranges, is key to maintaining occupant comfort year
round.
In the summer months, the thermal mass of a building (typically the walls and floors) absorb heat energy,
preventing it from overheating the space. Heavy weight construction elements are able to absorb large
amounts of energy with little rise in surface temperature, so there can even be an effect of radiant cooling.
At night when temperatures drop, the heat stored by the thermal mass is released warming the space and
cooling the mass, so that the process may be repeated the next day. This is particularly helpful when paired
with night-flush ventilation. In the winter months, the thermal mass can collect heat from direct solar
radiation and store it until releasing it during the night (de Saulles 2012).
The Spanish in early California used adobe to create thermally massive structures with great success. The
large houses of rancheros were either built on the ground, thus harnessing the thermal mass of the earth
itself, of were constructed on heavy plinths, which also typically added thermal mass. The smaller homes
in Spanish settlements were also typically adobe, under low tar-covered roofs (Oliver 1997). The walls
themselves were thick and made of adobe masonry. The surface treatment of this adobe also helped in
managing heat gains by being highly reflective. One observer, upon seeing a Spanish settlement north of
Los Angeles in 1883, noted, “when entered it consists almost exclusively of white-washed adobe houses”
(Oliver 1997). However, the widespread use of reflective coatings may be more coincidence than conscious
design on the part of Spanish designers: one of the statues of the Law of the Indies was that a settlement’s
“buildings be all of one type for the sake of the beauty of the town”(Mundigo and Crouch 1977).
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Figure 11: The Avila Adobe, the oldest building in Los Angeles. Note the white walls and veranda
for shade (Dickerson, n.d.)
The materials available to designers of buildings in the early twentieth century was considerably greater,
allowing for different forms and aesthetics to grace Los Angeles. One of the most influential materials was
steel, which allowed designers to build higher buildings with thinner walls than would have been necessary
with the previous method of large-scale construction: load-bearing masonry. However, masonry still
remained in extensive use which does allow for the use of thermal mass.
2.2 Mechanical Air Conditioning
World War II marks an important shift in how buildings were designed and used. It was around this time
that mechanical heating, ventilation, and air conditioning systems became widespread and part of the
conventional building paradigm. The effects of these systems on occupant comfort, energy use intensity,
and how the buildings themselves were constructed had a profound impact on the built environment, and
the consequences of this shift, for good or ill, are perhaps even more profound today.
2.2.1 History
The modern air conditioner was invented by Willis Haviland Carrier at the turn of the twentieth century.
Humidity and changing thermal conditions interfered with the operations of a Brooklyn publishing company,
causing waste of paper, ink, and time. Carrier was asked to find a solution to dehumidify the printing plant.
His solution called for cooling the air below its dew point by forcing air over a cast iron grille fed with cold
water. Though the system did not perform as well as Carrier expected (relative humidity was only reduced
from 86% to 63%) it soon became an essential part of the industrial sector’s operations (Nagengast 2002).
Though there was widespread use of mechanical air conditioning for industrial applications since its first
use at the Brooklyn printing plant in 1902, the technology did not gain traction in other building sectors until
the mid-century. From the post-war boom of the late 1950s to 1980, the US saw an enormous rise in
residential air conditioners (from less than one million units in 1955 to nearly 40.4 million units by 1980)
(Biddle 2008). Some historians argue that the primary catalyst for this was a change in construction
techniques, which allowed for houses to be built more cheaply while easily integrating central air systems.
Others point out changes to mortgage regulations that allowed these systems to be financed along with the
house as a whole. Additionally, the increase in real incomes throughout the 1960s and the falling costs of
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energy seem to play a significant role in the adoption of both central and room air conditioning systems
(Biddle 2008).
The environmental and energy costs of air conditioning have not been much of a concern for most of the
twentieth century. However evidence suggesting a warming climate and a deteriorating ozone layer
discovered over the last thirty years has caused many to take a second look at air conditioning. In the US,
more than two-thirds of households have air conditioners. The cost of running and maintaining these units
exceeds $11 billion per year. The US Department of Energy estimates that 5% of all electricity produced
in the country is used by air conditioners, resulting in 100 million tons of carbon dioxide emissions annually
(DOE 2012b).
2.2.2 System Types
There are many different kinds of air conditioning systems available to best capitalize on the opportunities
and challenges of particular climates, building types, and budgets. The first consideration when choosing
among air conditioners is whether the system be split or packaged. Split systems, as the name implies,
have the components split between two parts, with an outdoor unit that houses the compressor and
condenser and an indoor unit that distributes the air and can be connected to a furnace or heat pump. A
packaged system, on the other hand, has all of the system components packaged into one unit (DOE, n.d.).
Air conditioners are further categorized among four types: central, room, ductless, and evaporative coolers.
A central system uses ducts and registers to move air throughout a space. It provides even cooling, is
quiet, and is more efficient than most smaller systems. A single room air conditioner can be installed in a
window or can be portable. They are small and cheap, however air leaks are common when they are not
installed with care. A ductless system is typically mounted on a wall and provides cooling to areas without
the need for ductwork. While they are easy to install and they are relatively efficient, they are expensive.
Finally, an evaporative cooler (also known as a swamp cooler) uses evaporated water to cool air, which it
then moves through the space. These systems are cheap and use less energy than conventional air
conditioning systems, but are only appropriate for climates with low humidity (DOE, n.d.).
2.2.3 Implementation in Buildings
An air conditioner is only valuable if it is able to bring internal temperatures down. This is not a problem for
new construction projects, where the building is designed to have an active cooling system from day one,
with all of the necessary ductwork and openings. In the conventional process for new construction, an air
conditioner is sized by a mechanical engineer based on the prescriptive requirements of the local building
code and/or ASHRAE. The sizing of these systems almost always includes a factor of safety, oversizing
the system for potentially high, but unlikely cooling loads.
The process becomes much more complicated when air conditioning is added to an existing building which
was not built with these systems in mind. According to Sharon C. Park, FAIA, the addition of new HVAC
system to historic properties is expensive and risky if proper care is not taken when making decisions.
Challenges, particularly when installing the system in a sensitive way (typically out of sight), should be
confronted carefully, bearing in mind that any action taken should cause no harm and be reversible (Park
1999).
The National Park Service has released a Preservation Brief to specifically address the topic of adding
HVAC systems to historic buildings. After providing a general breakdown of the different types of systems
and where they might be appropriate, a list of do’s and don’ts is provided to help property owners and
design teams working through the retrofit process. The first and most important suggestion is to use
historically appropriate passive and non-mechanical means of reducing heating and cooling loads before
installing an HVAC system. It is only advisable to add new systems when they are absolutely necessary
(Park 1991).
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2.3 Building Retrofits
Retrofits are an important part of the built environment yet at times the definition can be unclear. In general
terms, to retrofit is “the act of adding a component or accessory to something that did not have it when
manufactured” (Oxford University Press, n.d.). With regard to buildings, a retrofit is generally carried out to
bring the building up to a level of compliance with building codes or to add new functionality. The scope
for building retrofits can vary tremendously, from minor intervention to complete building overhauls.
Regardless of scope, however, these retrofits typically contribute to increasing a building’s useful life and
will be discussed further in the following sections.
2.3.1 Building Lifecycle
Like everything else produced by man, buildings have a finite lifecycle. Based on building application and
the assessments of various tax and insurance agencies, different buildings can have different assumed
periods of useful life. A commercial building, for example, is estimated to have a useful life of 39 years by
the US Internal Revenue Service while a residential rental building has a useful life of 27.5 years (Lander
2014). The actual life of a commercial building has been assessed by researchers at the Department of
Energy, with the median lifetime being 70 to 75 years. Unfortunately, many buildings are not able to reach
this lifespan since demolition before the exhaustion of useful life is common (Kestner and Webster 2010).
From a sustainability standpoint, maximizing a building’s lifespan is an important part of limiting the built
environment’s effect on the planet. The phrase which now sums up the connection between existing
buildings and sustainability is almost a cliché: “The greenest building is… one that is already built” (Carl
Elefante, FAIA Quinn Evans|Architects). This is because an existing building’s embodied energy, which
includes all of the energy (direct or indirect) used to acquire materials, process them, and install them in a
building, is significant and wasted if the building is torn down and replaced (Carroon 2010). In the US, a
typical 50,000ft
2
commercial building has an embodied energy of 80 billion Btu (ACHP 1979). Almost all of
that energy is lost if the building is torn down, then more energy must be spent to get the materials and
construct a new building. According to the United Nations Energy Programme, the embodied energy of a
building represents about 20% of its lifetime energy consumption, if that building is in operation for 100
years. Since most buildings in the US do not last even half that long, the significance of embodied as a
percentage of total energy used becomes even greater (Carroon 2010).
As a building ages, it can develop strong ties to the city and culture in which it was constructed, which is
another reason why existing buildings are often spared demolition and retrofit instead. Buildings can earn
protected historic status under the National Historic Preservation Act of 1966 by being listed on local, state,
and or federal registries of historic and cultural monuments. Generally for a building to be considered for
historic status, it must be fifty years old or older and still look much like it did in the period of its historic
significance (National Park Service 2014a).
The phrase “long life/loose fit” can be used to describe a building’s ability to have a long lifespan while not
becoming functionally obsolete. The term was first used by Stewart Brand in his book How Buildings Learn:
What Happens After They’re Built and proposes that building can and should be used for a long time, but
that they should be used in a flexible way which allows for different functions over time (Carroon 2010).
2.3.2 Intervention Types
There are many different types of retrofits that can be undertaken in a building, varying in scope, intensity
and final goal. Some of the most common retrofits include structural, fire and life safety, access for disabled
persons, thermal performance, and aesthetic (Martinez Arias 2013). The various levels of conservation
have been detailed by Georg Giebeler to try to clarify some of the requirements behind each of the
intervention types and allow for better planning by architects and builders. His terms are important for
understanding the various retrofit options available.
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Reconstruction can be used to classify new construction done to rebuild a previously existing building.
Though it is new construction and new materials, it is often carried out with traditional means of building.
Because the building is not original, it must comply with modern construction codes and standards.
Planning for these projects typically involves significant new work by the architect, since it is very rare for
previously-existing historic buildings to be documented in the detail required for their total reconstruction.
Restoration is closely related to reconstruction, however it involves using original building elements to finish
an incomplete structure. The goal is the preservation of aesthetic and historical value as much as can be
supported by original documents and evidence; guesswork should not be part of the restoration process
(Giebeler 2009, 11).
When an old building does not receive any new systems or functionality, the intervention can be classified
as renovation. Renovation could also be considered a kind of in-depth maintenance, meant to bring a
building back up to the level it was when it was new. After a renovation, regular maintenance will keep the
building functioning properly for its intended use. Repairs are often a necessary part of this process, in
which there is limited replacement to damaged systems. Refurbishment, on the other hand, involves the
outright replacement of outdated building systems. There are many degrees of refurbishment, ranging from
replacing single system components in specific building locations (partial refurbishment) to encompassing
the entire building or piece thereof as a complete and separate element (normal refurbishment) to complete
demolition/removal of an old system and its replacement (total refurbishment). In any case, refurbishment
remains similar to renovation in that basic building use and structural systems remain unchanged. In
contrast, conversion always involves changes to the structural system and is often used in conjunction with
total refurbishment (Giebeler 2009, 12-14).
Modernization is a related term which describes improving “the lettable [leasable] floor space by increasing
the level of comfort or decreasing the running costs” (14). This can involve partial refurbishment or total
refurbishment and conversion. Interventions considered partial refurbishment could include upgrades to
thermal insulation and replacing windows. Total refurbishment and conversion could include installing an
elevator, adding balconies, creating new restrooms, and changing/adding electrical and data systems
(Giebeler 2009).
2.3.3 Performing an Energy Retrofit
Energy retrofits to historic buildings can typically be considered as refurbishment, with varying degrees of
conversion. Modernization is the conventional practice for energy upgrades, however it is important to note
that this research explores the impacts of stepping back from the modern, business as usual building
treatments and systems back to strategies that were common before the modern dependence on active
building systems.
Regardless of exactly what is hoped to be accomplished with an energy retrofit, the process follows the
same workflow documented by Giebeler in his Refurbishment Manual. The first step of the process is to
analyze the existing building. While a complete analysis of the building is cost prohibitive, carefully selecting
critical points to check can be sufficient in developing an actionable plan for the retrofit. This analysis
includes research into original drawings and as-built plans, visual inspections, invasive (destructive)
inspections, and measurement and laboratory analysis. The second step is to evaluate the collected
evidence to determine if the client’s goals can be achieved for the retrofit within a reasonable budget and
timeframe. The evaluation should focus on the building’s current and future use, the feasibility of its
conversion, and the patterns of past and potential future damage. The final step, carrying out the actual
planning process, can take this information to make a feasible project (Giebeler 2009, 22-27).
The hierarchy of goals for the energy retrofit of a building is the same used for designing a brand new
building. According to the US Energy Information Administration and Norbert Lechner, the first
consideration for addressing energy consumption and carbon emissions is to improve heat rejection,
retention, and avoidance thus reducing the energy demand. The critical decisions for this tier are made
27

before ground is broken for the building and include site and orientation, window-wall ratio, and wall
construction. Other measures that are easier to implement during a retrofit include improving insulation,
improving glazing properties, and ensuring that all required appliances are as efficient as possible. The
second step is to then consider passive technologies to harness natural forces to be used in the building.
Strategies such as earth-coupling, light shelves for daylighting, and utilizing thermally massive features
would be explored at this stage. Finally, once all efficiency and passive options have been explored, new
and efficient building systems should be sized to meet the reduced building loads and on-site renewable
energy can be assessed to meet those loads (Lechner 2009; EIA 2011).
Unfortunately, it is not uncommon for energy retrofits to put most of the project’s design effort and money
into the third tier, mechanical systems. While a more efficient system will use less energy than an inefficient
one, energy is still wasted if the loads themselves are not addressed (Martinez Arias 2013). Not only does
addressing the loads minimize energy consumption, it also decreases the needed capacity and cost of the
systems need to maintain thermal comfort.
Luckily, there are examples of successful energy retrofits which addressed reducing initial demand, then
utilized more efficient systems. The first is the Edith Green-Wendell Wyatt Federal Building in Portland,
Oregon. This 18 story office tower was originally built in 1974 and retrofitted under the American Recovery
and Reinvestment Act in 2013. By first addressing issues like shading, proper orientation for daylighting
and light redirection, and window-wall ratio and then implementing new HVAC systems and on-site
renewable energy, the previously existing energy hog now consumes 60-65% less energy than a typical
office building (SERA Architects 2014; Welcome to the “New” Edith Green-Wendell Wyatt Federal Building
2014). Another example of a successful energy retrofit is the Empire State Building in New York City. This
project is of particular importance because of its status as a National Historic Landmark. Care had to be
taken to maintain the character-defining features of the building, but through interventions such as
retrofitting the windows and adding insulation to the envelope, the building was able to reduce its energy
consumption by 38% (Empire State Realty Trust 2014).
2.3.3.1 Energy Conservation Measures
Energy retrofits are conducted through a mixture of energy conservation measures, or ECMs. An ECM is
defined as “an installation or modification of an installation in, or a remodeling of, an existing building in
order to reduce energy consumption and operating costs” under Title I of the Ohio State Code (State of
Ohio 2012). ECMs can address any aspect of a building, so even an incomplete list of them could be very
long. The following are selected examples from a list of ECMs compiled by the Maryland Energy
Administration.
Occupant behavior is an important part of reducing a building’s energy consumption. Since implementation
of behavioral changes also tends to be free, it should be considered for most energy retrofits. Examples of
this kind of ECM include turning off office equipment such as computer, printer/copiers, and monitors at the
end of the day or any other piece of equipment when not in use. An even simpler behavioral change is
ensuring that occupants report burned-out and flickering lamps, since a lamp does not have to be functional
for the ballast to continue consuming energy. Finally, adjusting and using appropriate schedules for lighting
and HVAC use is an effective behavioral ECM (Maryland Energy Administration, n.d.). A case study of the
Willett Center in Rome, New York confirms the value of behavior changes as ECMs. Simply by adjusting
schedules during non-peak hours, the Willett Center was able to achieve a total electricity savings of 2 to
5% and a total gas savings of 10 to 20%. Since these changes are free, these are pure savings with nothing
to payback (Mininni et al. 2009). Some of these practices go hand-in-hand with the ECMs in the category
of maintenance and building tuning. Setbacks for supply air and hot and cold water loops that allow systems
to more closely match zone loads are simple but effective ECMs, especially when coupled with regular
cleaning and maintenance (Maryland Energy Administration 2008).
28

Figure 12: Effect of various ECMs on Willett Center (Mininni et al. 2009)
Lighting ECMs most often take the form of changing the existing systems to a more efficient technology.
For example, one of the first ECMs on Maryland’s list to address lighting is to use fluorescent technology,
including, “new T8, T5, compact fluorescents, specular aluminum reflectors, and electronic ballasts”
(Maryland Energy Administration 2008). There are passive lighting ECMs as well, such as installing or
adjusting windows, adding skylights, and using proper shading devices. These measures can be used in
conjunction with the advanced lighting systems to automatically use artificial light only as it is necessary
(Maryland Energy Administration 2008).
There are many ECM options for HVAC systems and accompanying fans and motors. As with the lighting
systems, most ECM options involve upgrading an old system to a new one with greater efficiency. One of
the suggestions given is to convert constant air volume systems to variable air volume systems. Another
suggestion is to reconfigure the ductwork to create new, more efficient zones (Maryland Energy
Administration 2008). Maryland’s list does not, however, provide information on some of the newer, more
sustainable conditioning technologies, such as waste heat recycling and radiant beams and floors.
The building envelope is a very important factor when assessing a building’s energy use and waste. It
becomes even more important when a project wants to use as many passive strategies as possible for
maintaining thermal comfort. Changing windows for new glazing types is a very common practice. Single-
pane windows can be replaced with double or triple-pane insulated glass, with additional treatments for
solar heat gain coefficient and visible light transmittance. Adding insulation to roofs and walls is also a
common practice. Georg Giebeler has assessed many common envelope types and compared thermal
properties before and after refurbishment. The decreased U-values of the wall and roof assemblies are
necessary to control the passage of heat and minimize demand on building systems. Airtightness is an
important thing to consider when intervening with the envelope and is considered another essential ECM
(Maryland Energy Administration 2008). The benefits of airtight construction include reduced heating loads,
no drafts, minimized moisture damage due to condensation, and better sound insulation (Giebeler 2009,
36).
ECM Description
Total Elec.
Savings
Total Gas
Savings
Equipment
Cost
Cost and Savings
Measurement Other Requirements
Payback
(years)
Green Scale
(5= most
green)
Adjust operational hours during non-peak periods (Nov. - Mar.) 2-5% 10-20% $0 Behavior Change Adjust automatic lighting timers 0 5
CO2 Sensor to control HVAC cycles 3-5% 3-5% $300-500 Per sensor Tie to RTU economizer 2 4
Utilize window shades more efficently for heating/cool 0% 10-20% $500 per 4'x12' system Timer/controller to coordinate blinds 3 4
Improve exterior and interior door seals 1% 1% Cont. Req. All walls Downtime/inconvenience N/A 2
Adopt Energy Star Products 2% 0% $350 Avg. per product Properly recycle/dispose old equipment 5 2
Inspect ductwork for additional repairs 1-3% 2-5% Cont. Req. All ducts Downtime/inconvenience N/A 2
Install ceiling fans in exhibit sections 1% 3% $100 Per fan Professional installation may be req. 3 4
Upgrade HVAC control system software 0.3% 0.5% $800 For system Contact provider 8 3
Solar Power exterior lights 0.1% 0% $75 Per unit Minimal Maintenance 3 4
Power factor correction device to level energy demanded 1-2% 0% $300 For system Professional installation may be req. 3 2
29

Figure 13: Typical U-values for old and refurbished wall assemblies (Giebeler 2009)
There are other ways to reduce loads in a building that are not as deeply systems-focused. A simple ECM
suggested by the Maryland Energy Administration is to use Energy Star certified appliances and pieces of
equipment. However, based on the Willett Center case study, the benefits to this ECM appear to be
minimal: 2% of total electricity savings with a five year payback period (Mininni et al. 2009). As with the
retrofit hierarchy discussed in section 2.3.3, on-site renewable energy sources are considered after all other
ECMs have been assessed. Maryland lists all of the most common energy sources, including solar-thermal,
photovoltaic arrays, wind, and fuel cells (Maryland Energy Administration 2008).
2.3.3.2 “Business as Usual” Retrofits
Most of the ECMs discussed above would be appropriate for a business as usual (BAU) energy retrofit. A
BAU retrofit is a retrofit carried out with conventional methods of building improvement. It is heavily guided
by prescriptive standards (from industry associations such as ASHRAE) and are not necessarily specifically
tailored to the building in question. These retrofits are typically effective at meeting loads with systems that
are highly efficient, and systems consume much of the project’s effort and budget. By comparison, efforts
to reduce the loads in the first place are considerably less. A BAU retrofit for a commercial office building
might include tightening the building envelope, installing more efficient lighting systems, and installing
mechanical systems with greater efficiency. The addition of on-site renewable energy, specifically
photovoltaic technology, is increasingly becoming a BAU practice, though this is still in the early stages and
does not have the prescriptive standards behind it that other building elements, such as HVAC systems,
do.

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2.3.3.3 “Environmentally Responsive” Retrofits
Environmentally responsive retrofits represent a different approach to intervening in existing buildings.
While BAU retrofits are driven by prescriptive standards, environmentally responsive retrofits are driven by
research. Many of the strategies used for these retrofits are taken directly from the example set by
vernacular architecture. Passive solar heating and cooling, natural ventilation, daylighting, and the
harnessing of thermal mass are critical for the execution of an environmentally responsive retrofit. These
retrofits might also be termed as bioclimatic, which describes a connection between human interaction with
the space and the space’s interaction with the environment (Hyde et al. 2009).
The strategies used for an environmentally responsive retrofit need not always be completely passive.
Often active and passive systems work together to maintain the comfort of building occupants while
consuming the smallest possible amount of energy. One example of this is mixed-mode ventilation, in
which natural ventilation is used when the outdoor air can support a comfortable indoor environment. When
necessary, the outdoor air can be fan-forced through the building or if the air is too warm, mechanical
cooling systems can be used. Another example of this is lighting systems capable of determining buildings
areas that are adequately daylight and automatically deactivating the artificial lights in those specific areas.
The key for environmentally responsive strategies to succeed is a feedback loop through which the
building’s own conditions can be compared with external conditions and take appropriate action. Whether
or not the action is automatic or manual is not the critical issue; whether or not outdoor conditions are
allowed to affect indoor conditions is.
2.3.3.4 Considerations for Registered Historic Buildings
Teams seeking to perform energy retrofits to buildings with registered historic status face more challenges
than simply how to reduce energy consumption. Federal law protects buildings and place considered to be
historic resources, and requires their preservation for future generations (WBDG Historic Preservation
Subcommittee 2014). The US Secretary of the Interior has developed Standards for the Treatment of
Historic Properties to assist property owners and protect historic character.
The Standards are divided into four broad treatment actions: preservation, rehabilitation, restoration, and
reconstruction. The treatment to be considered for projects pursuing energy retrofits is rehabilitation, which
“acknowledges the need to alter or add to a historic property to meet continuing or changing uses while
retaining the property's historic character” (National Park Service 2014b). The ten Standards for
Rehabilitation are:
1. A property will be used as it was historically or be given a new use that requires minimal change to
its distinctive materials, features, spaces, and spatial relationships.
2. The historic character of a property will be retained and preserved. The removal of distinctive
materials or alteration of features, spaces, and spatial relationships that characterize a property will
be avoided.
3. Each property will be recognized as a physical record of its time, place, and use. Changes that
create a false sense of historical development, such as adding conjectural features or elements
from other historic properties, will not be undertaken.
4. Changes to a property that have acquired historic significance in their own right will be retained and
preserved.
5. Distinctive materials, features, finishes, and construction techniques or examples of craftsmanship
that characterize a property will be preserved.
6. Deteriorated historic features will be repaired rather than replaced. Where the severity of
deterioration requires replacement of a distinctive feature, the new feature will match the old in
design, color, texture, and, where possible, materials. Replacement of missing features will be
substantiated by documentary and physical evidence.
7. Chemical or physical treatments, if appropriate, will be undertaken using the gentlest means
possible. Treatments that cause damage to historic materials will not be used.
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8. Archeological resources will be protected and preserved in place. If such resources must be
disturbed, mitigation measures will be undertaken.
9. New additions, exterior alterations, or related new construction will not destroy historic materials,
features, and spatial relationships that characterize the property. The new work will be differentiated
from the old and will be compatible with the historic materials, features, size, scale and proportion,
and massing to protect the integrity of the property and its environment.
10. New additions and adjacent or related new construction will be undertaken in such a manner that,
if removed in the future, the essential form and integrity of the historic property and its environment
would be unimpaired.
Quoted from the National Park Service (Grimmer et al. 2011)
Further guidelines have been developed by the National Parks Service to specifically address the
sustainability and energy issues faced by historic buildings. One of the continuing themes through all of
the Standards is the need to retain existing building fabric, and to repair rather than to replace building
elements whenever possible. For example, it violates the standards to replace existing historic windows in
good or repairable condition with new insulated or high performance windows (Grimmer et al. 2011). The
Illustrated Guidelines on Sustainability cover the topics of planning, maintenance, windows, weatherization,
insulation, HVAC, solar technology, wind power, roofs, site features, and daylighting.
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3 Methodology
The State of California has mandated that carbon emissions reductions be 80% less than 1990 levels by
2050 (Air Resources Board 2014). To reach this target, all of California’s existing building stock must be
retrofit to become 40% more efficient by 2030 (Long 2011). Local governments must develop methods for
meeting these goals, and it is the intent of this research to determine if the City of Los Angeles can meet
this goal for existing buildings with the reintroduction of passive strategies. The hypothesis of this research
is that the reintroduction of passive strategies in pre-war buildings (which were originally designed to use
passive strategies out of necessity) will lead to efficiency improvements greater than or equal to 40%, thus
meeting the goal for this subset of the city’s building stock while avoiding the costs and risks associated
with conventional energy retrofits to historic buildings.
The addition of HVAC systems to “modernize” historic buildings can create serious problems. Successful
retrofits require careful planning to balance preservation objectives with interior climate needs (Park 1991).
Some historic buildings do not readily accept modern mechanical systems, and unfortunately, many
buildings have been retrofit without consideration for the historic fabric they contain. One problem in
particular is the result of sealing a historic building’s envelope. Often this is done in part by replacing
window assemblies, which is not an acceptable strategy (Park 1991). Operable windows might also be
sealed to enhance the envelope’s tightness: another objectionable retrofit choice. The move to seal an
envelope when adding an HVAC system comes from a simple, albeit flawed, idea that operable windows
waste energy. The view is that operable windows allow conditioned air to escape and unconditioned air to
enter a space, which requires the mechanical system to work harder. However, with careful planning,
operable windows can help enhance the energy efficiency of mechanical systems (Daly 2002).
A simulation methodology was developed to be able to compare a building’s current condition to an
environmentally responsive condition. An environmentally responsive building is one which is able to use
the surrounding climate (thermal conditions, light, environmental context/orientation, etc.) to keep internal
conditions comfortable with minimal use of supplemental mechanical heating, cooling, and lighting systems.
After the two conditions are simulated, determining the potential savings of one over the other is a matter
of simple arithmetic. However, as this result will apply to the single modeled condition, more testing is
required to determine the potential savings across the whole of Los Angeles’s pre-war building stock. Using
parametric analysis, further simulations determined potential savings for different window-wall ratios
(WWR), orientations, and shading conditions. When these results are applied to the building stock, total
savings, and whether or not Los Angeles can meet necessary emissions reductions, was determined.
3.1 Precedent Analysis
3.1.1 Multi-Building Assessment
Before simulations can be run, it is essential to understand the relevant attributes of pre-war buildings that
impact daylighting and energy performance. Therefore, the first part of this research consisted of an
assessment of buildings in downtown Los Angeles built in the first thirty years of the twentieth century.
These buildings were selected based on recommendations from preservation architects and faculty in the
Heritage Conservation program at the University of Southern California. Characteristics considered
particularly important were location, building footprint (size and shape), and whether or not the building had
been retrofit since its initial construction. The building’s current program was not a factor in choosing
buildings to assess, nor was the building’s status as a Historic-Cultural Monument. The buildings selected
for assessment include:

33

Table 1: Selected Pre-War Buildings
Hotel Alexandria

Year: 1906
WWR: 0.25
Op Win, 1906: Yes
Op Win, now: Yes
Ext. Shading: No

PacMutual Building

Year: 1926
WWR: 0.46
Op Win, 1908: Yes
Op Win, now: No
Ext. Shading: No

May Company Building

Year: 1908
WWR: 0.30
Op Win, 1908: Yes
Op Win, now: Yes
Ext. Shading: No

Jewelry Trades Building

Year: 1912
WWR: 0.42
Op Win, 1912: Yes
Op Win, now: Yes
Ext. Shading: No

34

Broadway Department Store

Year: 1914
WWR: 0.26
Op Win, 1914: Yes
Op Win, now: No
Ext. Shading: No

Biltmore Hotel

Year: 1923
WWR: 0.26
Op Win, 1923: Yes
Op Win, now: No
Ext. Shading: No

Pershing Square Building

Year: 1923
WWR: 0.24
Op Win, 1923: Yes
Op Win, now: No
Ext. Shading: No

Subway Terminal Building

Year: 1925
WWR: 0.29
Op Win, 1929: Yes
Op Win, now: Yes
Ext. Shading: No

35

Jonathan Club

Year: 1925
WWR: 0.18
Op Win, 1925: Yes
Op Win, now: No
Ext. Shading: No

State Theatre

Year: 1930
WWR: 0.36
Op Win, 1930: Yes
Op Win, now: Yes
Ext. Shading: No

Eastern Columbia Building

Year: 1930
WWR: 0.32
Op Win, 1932: Yes
Op Win, now: Yes
Ext. Shading: No

The goal of the assessment was to determine the design strategies used in a typical pre-war building in
Los Angeles. The hope was to get information that would lead to the development of a representative
energy simulation model, allowing results to be applied to the whole subset of buildings. Key characteristics
were evaluated to drive this development.
Orientation – The effect of downtown Los Angeles’s tilted street grid on its buildings is an important
consideration. There does not appear to be a predominant orientation used in this part of Los Angeles.
Building Geometry – The pre-war buildings of downtown Los Angeles come in a wide range of shapes that
affect thermal and lighting performance. Large rectangular floor plans were common, as they still are today,
but alphabet shapes as floor plates could readily be seen. These shapes, with increased surface areas
and numerous wings, allowed for greater air and light penetration, making natural lighting and
cooling/heating strategies more effective.
36

Materials and Thermal Mass – The assemblies that compose a building play a big role in the building’s
thermal performance. A material’s ability to insulate a space or to let light through can have a major impact
on the final energy consumption. Many pre-war buildings in Los Angeles share material types and
construction techniques. Masonry, concrete, and other massive materials were common and acted as
banks for coolth when properly coupled with the interior space. These configurations are desirable as
passive strategies and understanding how common the practice was is important.
Window-to-Wall Ratio – The amount of glazing on a building’s exterior has a direct impact on the energy
and lighting performance of that building. The effects are positive and negative, ranging from allowing
usable daylight and winter solar gains to enter the space to overheating due to direct summer sun. Pre-
war buildings used less glass than is used now for a variety of reasons, but if architects at the time had an
optimal WWR to aim for, then that is the ratio to test.
3.1.2 Subway Terminal Building Case Study
The Subway Terminal Building (STB) at 417 S. Hill St. (now called Metro 417) was selected from among
the pre-war buildings evaluated in the first part of this research to act as a case study for further analysis.
The building is of the Italianate Beaux Arts style and was designed by architects Schultze and Weaver in
the heart of downtown Los Angeles. It was constructed in 1925 as a joint venture of the Pacific Electric
Railway and the Subway Terminal Corporation. It stands at twelve stories tall and has approximately
680,000 square feet of floor space. The building is laid out with five wings extending from a central spine
which runs parallel to 4th Street (NW-SE). Four wings (A, B, C, and D from west to east) extend to the
southwest, creating three light courts between them. The fifth wing (E) extends northeast, and is nearly in
line with wing B (McAvoy 2006).

Figure 14: Left- Engraved cornerstone; Right- view of STB from Pershing Square
The building originally served as the terminus for the Pacific Electric Hollywood Subway, which linked
Hollywood, Glendale, and the San Fernando Valley with Los Angeles’s growing downtown. The ground,
mezzanine, and basement levels of the building were used as public concourse for the 65,000 people that
rode the Hollywood Subway daily. The upper floors consisted of office space and made the building an
important commercial and transportation hub (McAvoy 2006).
37

Figure 15: Map showing the location of the Subway Terminal Building in downtown Los
Angeles (Google 2015)
The Subway Terminal Building features masonry, steel, and concrete construction, typical of the era.
Concrete foundations support the steel frame and masonry walls. The exterior is clad in granite and other
stone on the lower levels while cheaper, lighter terra cotta is used on the upper levels. The exterior walls
are, on average, 24 inches thick, but become thinner as the building rises. The steel frame carries the load
while masonry infill walls create the partitions. Windows are 4 ft wide by 6 ft 10 in tall, and are typically
arranged in pairs. The window-wall ratio is 0.3.

Figure 16: Photograph of original blueprints
38

Numerous factors went into the Subway Terminal Building’s selection:
Ease of access: John Lesak, the fourth seat on this thesis committee, has an office in this building. Having
a contact at the building makes site access much easier and can provide insight into how modern occupants
use the building and feel in the space.
Retrofit status: The Subway Terminal Building has been retrofit numerous times throughout its ninety year
life, most recently as part of a mixed use project converting the upper floors to apartments. Other retrofits
include the addition of ducted HVAC equipment and seismic/structural upgrades. These retrofits are good
examples of “modernization” programs that other buildings of similar vintage have likely gone through.
Original design features: One of the most readily recognizable features of the Subway Terminal Building
is its four-legged “E” shape (its southwest elevation). Five wings (four off pointing southwest and one
pointing northeast) extend from a central spine. This configuration allows for testing double- vs. single-
loaded corridors and the effect of a building shading itself or blocking its own natural ventilation.
3.2 Simulation
3.2.1 Approach
Energy and daylight simulation tools were used to quantify the impacts of reintroducing passive strategies
to pre-war buildings. Two “shoebox” models were compared against each other. The first is the business-
as-usual (BAU) model, which represents the building in its current state. This represents the STB as it
stands now, after numerous retrofits it has experienced since 1925. The notable changes from the original
design that are included in this model are the addition of mechanical conditioning systems and the sealing
of the envelope. As with many retrofits, the HVAC system was added with the addition of a dropped ceiling
to conceal the new duct work. This blocked the upper 18 inches of the windows, which remained minimally
operable (windows could open a maximum of 4 inches) but occupants only rarely used them anyway. The
other model was the environmentally responsive (ER) model, which represents the building with passive
strategies reintegrated into its daily operation. The models share orientation, geometry, and opaque
construction types, but mechanical system configurations and electric lighting schemes are changed in the
ER model to test the effectiveness of these strategies. The BAU and ER models were simulated using a
real climate (California Climate Zone 9) with an analysis period of one year.
Table 2: Characteristics shared by BAU and ER models
Size 15 ft wide by 22.5 ft deep by 12 ft tall
Orientation Variable
WWR Variable
Interior Wall
Construction
INTERIOR WALL (adiabatic)
Exterior Wall
Construction
STB_Wall_MasonryConcrete-2ft4in
Glazing
Construction
STB_Glazing_LowE-1pane
Interior Floor
Construction
INTERIOR FLOOR (adiabatic)
Ceiling
Construction
INTERIOR CEILING (adiabatic)

39

3.2.2 Simulation Software
Energy and thermal simulations were conducted with EnergyPlus, a program developed by the U.S.
Department of Energy. EnergyPlus is a robust calculation tool which determines the heating and cooling
energy necessary to maintain specified set points, the energy consumed through given building activities,
and the performance of other building systems. However, because the program only reads and outputs
text files, users must have other programs to act as a graphical interface (DOE 2013).
The backbone of the graphical interface used for these engines is Rhinoceros by Robert McNeel and
Associates. Rhino, as it is commonly called, is used extensively for architecture, industrial and automotive
design, 3D visualization and analysis, and graphic design and multimedia. A host of add-ons and plug-ins
enhance Rhino’s usability and draw professionals from various fields to the platform (Robert McNeel &
Associates 2014).
Grasshopper is a visual programming language also developed by Robert McNeel and Associates as a
plugin for Rhino that was essential for this research. It allows users to design algorithms with a node-based
editor in which components are linked together with a series of wires, feeding data through functions to
generate the desired outcome. When linked with Rhino’s powerful modeling capabilities, this allows
designers and architects to create complex forms with ease. Its computational powers can be used by
these same professionals to make changes to designs and see their effects in real time, be they changes
in terms of project aesthetics, project performance (factors like energy consumption, daylight, comfort, etc.),
or any other characteristics. Like Rhino, Grasshopper also has many plug-ins that add to its functionality
and allow it to perform a wide range of operations. Numerous plug-ins have been developed which are
aimed squarely at the architecture and engineering profession (Davidson 2014). The primary plug-ins used
in these analyses are Ladybug and Honeybee.
Ladybug and Honeybee are two separate environmental plug-ins for Grasshopper created by Mostapha
Sadeghipour Roudsari (http://www.food4rhino.com/project/ladybug-honeybee?ufh). Ladybug helps users
interpret standard EnergyPlus Weather files and visualize their results. Ladybug is able to produce graphics
for radiation analysis, solar analysis, and comfort analysis, allowing designers to make appropriate
decisions for their projects based on real climatic data. Honeybee is the user interface for executing
EnergyPlus simulations. It also links Grasshopper to Radiance, a simulation engine for daylight, as well as
additional tools for using these simulation engines (namely Daysim and OpenStudio) (Roudasri 2015).
The final software element that bears mentioning is not a new plugin, but rather a tool built with the
components of the plug-ins above to perform complex calculations for the metric of thermal autonomy. This
collection of components was developed by Alejandro Gamas in 2014 and used to calculate thermal
autonomy and generate graphics to illustrate it (Gamas Villamil 2014).
3.2.3 Model Geometry
Because the goal is to determine savings across an entire subset of Los Angeles’s building stock rather
than for a single building, the simulations were conducted on a generic, shoebox-type model. This model
represents a standard section of the Subway Terminal Building, but is nondescript enough to be able to
apply to other buildings of the same type and vintage. The materials and measurements used in the model
were taken from original plan documents, and changed as necessary to represent the current condition.
40

Figure 17: View of shoebox model from northeast with WWR of 0.30
Rhino and Grasshopper offer powerful tools for the generation and manipulation of geometry. Components
to automatically generate zones, fenestration, and shading devices are available to quickly create buildings
or test zones with a few simple user inputs. The geometry for the shoebox model was first created with an
oblong box in Rhino, 15 feet by 22.5 feet. Though the STB is oriented along the titled Spanish grid of
downtown Los Angeles (the grid is oriented 36 degrees east of north; the STB’s primary orientation is 126
degrees east of north), the shoebox was drawn at 0 degrees (window facing north). The models will be
tested at 45 degree intervals, so the initial orientation is not a concern. Floor-to-ceiling and floor-to-floor
heights were 10 feet and 12 feet, respectively. These dimensions were derived from the actual Subway
Terminal Building. Each surface was individually assigned attributes using the Honeybee Create Surfaces
component. Glazing was assigned based on a WWR of 0.3 on one wall only, with the Honeybee Glazing
based on Ratio component. Since the zone is modeled independently, only one story was created, but the
floor and roof constructions are modeled as adiabatic.
Custom constructions were created to best represent the building as it currently exists. Custom materials
were created with the Honeybee EnergyPlus Opaque Material component to match the STB’s masonry and
steel construction and terracotta cladding. Custom glazing constructions were created as well, to represent
the building’s single pane windows with an added low-e film.
Table 3: Opaque Constructions
Construction Material Thickness
Rmat
(ft
2
∙ °F∙h /Bt u )
RTot
(ft
2
∙ °F∙h /Bt u )
STB_Typical-Wall
Terracotta 2 in 0.7531
4.1208 Masonry 18 in 2.9170
Gypsum Board 0.5 in 0.4507
INTERIOR WALL
Gypsum Board 0.75 in 0.6743
2.2003 Air Space not given 0.8517
Gypsum Board 0.75 in 0.6743
INTERIOR FLOOR
Acoustic Tile 0.75 in 1.8076
3.9182
Air Space not given 1.0221
Lightweight
Concrete
4 in 1.0885
INTERIOR FLOOR
Lightweight
Concrete
4 in 1.0885
3.9182
Air Space not given 1.0221
Acoustic Tile 0.75 in 1.8076

41

Table 4: Glazing Assembly
Construction Material UTot SHGC TVis
STB_Glazing_LowE-1pane Single pane with Low-E film 0.2888 0.39 0.61

The zone was assigned a building program based on default options available through the Honeybee plug-
in. The Office building program was selected, and assigned schedules for occupancy and equipment to
match those used in a traditional office environment. The loads and most schedules associated with the
Office program did not change between the BAU and ER models.
Table 5: Loads and Schedules
Equipment Loads 4.46 W/ft
2

Lighting Power Density 1.2 W/ft
2

People 0.05 people/ft
2

Infiltration Rate 0.0007 ft
3
/s∙ft
2

Ventilation per Person 0.0079 ft
3
/s∙ft
2

Ventilation per Area 0.0010 ft
3
/s∙ft
2

Occupancy Medium Office Bldg Occ
Activity Medium Office Activity
Equipment Schedule Medium Office Bldg Equip
Infiltration Schedule Medium Office Infil Quarter On
HVAC Availability Schedule ALWAYS ON
Cooling Set Point Variable
Heating Set Point Variable
3.2.4 Variables
The variables to be tested on the ER model are orientation, WWR, photosensor-controlled, and perhaps
most importantly, use of natural ventilation in the form of a mixed-mode system. The first two variables are
contextual variables. They are used to determine how the results of the simulation comparisons can be
applied to the wider building stock. The last two variables are the actual interventions, and represent work
that could be carried out on the Subway Terminal Building, or any other building for that matter, in an
attempt to improve energy efficiency. The variables and the exact differences between the BAU and ER
models are discussed below.
3.2.4.1 Interventions
Lighting – Lighting is one of the greatest end uses of electricity in office buildings, and often a significant
portion of this energy expenditure is unnecessary. According to the International Dark-Sky Association,
one third of lighting in the U.S. is wasted at the cost of $2.2 billion, 12.9 million barrels of oil, and 3.6 million
tons of coal (International Dark-Sky Association 2014). Systems that can determine when electric lighting
is needed and accordingly control its activation are an important part of managing energy used for lighting.
The variable used to represent lighting controls in the ER model is quite simple. The lighting power density
for both the BAU and ER models is the same, but the ER lighting total is reduced to represent lighting
controls. The calculated daylight autonomy represents the time that the space is adequately lit with daylight
and for the rest of the time, electric light must be used (see section 3.2.5.2). Therefore, the daylight
autonomy is used as a savings factor to reduce the total lighting load (this is further discussed in Metrics).
Natural Ventilation - Many pre-war buildings in downtown Los Angeles have been retrofit to introduce
mechanical HVAC systems. It is standard practice for this retrofit to include sealing the building envelope,
because operable windows make mechanical system sizing more complex due to the advanced calculation
required and because management of this type of system can be more difficult, especially if occupants are
42

given individual controls. Because building owners typically avoid this complexity, occupants cannot take
advantage of the free cooling (or heating) available from the surrounding environment. Previous research
suggests that replacing conventional mechanical systems with natural ventilation or mixed-mode systems
can yield HVAC energy savings of 47-79% (Center for Building Performance and Diagnostics 2004).
Because the savings are potentially very high, the use of a mixed-mode system is a very important
difference between the BAU model (which does not use it) and ER model (which does).
Mixed-mode ventilation is a form of space conditioning which uses natural ventilation from operable
windows or other passive air intakes in conjunction with a mechanical system for increased ventilation and
cooling (Brager 2006). Ideally, theses system use natural ventilation to keep the space comfortable when
external conditions make that feasible, and the mechanical system supplements the natural ventilation
when it is not adequate to maintain comfort. Mixed-mode buildings typically utilize one of three schemes:
concurrent, change-over, and zoned. Concurrent systems are the most common, and consist of operable
windows and mechanical systems in operation at the same time in the same space. Change-over systems
use one method (natural or mechanical), at a time and change over to the other upon reaching a set
threshold. This threshold could be a given temperature, occupancy level, or time of the day or year. The
ER model utilizes this kind of mixed-mode ventilation (Fig. 18). A zoned system is one in which different
parts of the building use different cooling strategies, such as cases where supplemental cooling is provided
only to conference rooms.

Figure 18: Diagram illustrating change-over mixed-mode system, where
natural ventilation or mechanical ventilation are used separately (Center for the
Built Environment 2013)
The first consideration for modeling BAU and ER practices is the set points and set backs of each model.
A set point is the temperature at which the mechanical system will begin heating or cooling. A set back is
the temperature to which the space will be heated or cooled while it is unoccupied. The set points and set
backs for the BAU and ER models are significant factors for energy performance. The BAU model includes
a heating set point of 69.8°F (21.0°C) and a heating setback of 60.08°F (15.6°C). The BAU cooling set
point is 75.2°F (24.0°C) and the cooling set back is 80.06°F (26.7°C). These values are input as annual
hourly schedules, where the set point is active from 6am to 10pm and the set back temperature is used for
the remaining hours. This was the default schedule used for the office program and the number of set point
hours was not adjusted from the BAU model to the ER model. Set points and setbacks for the ER model
were also represented in annual hourly schedules. The cooling set point and set back was 81.0°F (27.22°C)
and 86.0°F (30.0°C), respectively, and the heating set point and set back was 68.0°F (20.0°C) and 60.08°F
(15.6°C). The set points and set backs are summarized in the following table:

43

Table 6: Set points and set backs
Model
Cooling
Set Point
Cooling
Set Back
Heating
Set Point
Heating
Set Back
BAU 75.2°F (24.0°C) 80.06°F (26.7°C) 69.8°F (21.0°C) 60.08°F (15.6°C)
ER 81.0°F (27.22°C) 86.0°F (30.0°C) 68.0°F (20.0°C) 60.08°F (15.6°C)

The different set points between the models is possible because of how occupants experience thermal
comfort. According to Standard 55, thermal comfort is “the condition of mind that expresses satisfaction
with the thermal environment and is assessed by subjective evaluation” (ASHRAE and ANSI 2013). The
range of temperatures in which occupants will feel this satisfaction is not necessarily the same year round.
The idea that the comfort range can change with the outdoor temperature is called adaptive thermal
comfort. The comfort range follows average outdoor temperatures, meaning that as temperatures rise
outside, occupants are able to feel satisfied in warmer interior conditions and as temperatures outside fall,
occupants will call cooler temperatures comfortable (ASHRAE and ANSI 2013). This model is incorporated
in the ASHRAE 55 standard and makes special considerations for naturally ventilated buildings. This is
because the relationship between outdoor conditions and thermal comfort is twice as strong as with
conventionally conditioned spaces. Research has shown that occupants of naturally ventilated spaces are
more tolerant of a wider range of interior temperatures due to changes in behavior and physiological
responses (de Dear and Brager 1998). Because of this wider range of tolerance, natural ventilation can be
used more to condition the space, thus greatly reducing energy consumption. The wider range of
acceptable temperatures represents annual averages. This makes for a simpler and more conservative
simulation, however more precise results could be achieved through a schedule that adjusts the set points
for monthly averages, instead of annual ones.
The latest release of Honeybee (0.0.56) provides a component for simulating natural ventilation. It provides
a significant improvement over the previous strategy for modelling natural ventilation in Honeybee, which
required increasing the infiltration rate to simulate open windows. The new component can simulate three
different types of natural ventilation: window, custom stack/wind ventilation, and fan driven (Fig. 19). For
this analysis, window natural ventilation was used.

Figure 19: Types of natural ventilation available in Honeybee
The window natural ventilation component simulates both buoyancy driven flow and wind driven flow. Wind
driven flow is the result of pressure generated by the wind on the building, which drives air through building
openings. Generally, positive pressure is created on the windward face, while negative pressure is created
on the roof and opposite side (Baker 2015a). Wind driven flow can be classified as either cross-ventilation
or single-side ventilation. Both models were simulated as single-sided zones. The equation is given as:

44

where,
Qw = wind driven ventilation rate (m
3
/s)
Cw = the fraction of operable glazing area
Ao = opening area (m
2
)
Fs = opening area fractional schedule
V = wind speed (m/s)

Buoyancy driven flow, also called stack ventilation, is the result of differences in vertical pressure. As the
temperature of air in a space increases, that air will rise and try to escape from openings high in the building.
The upward movement of this air creates negative pressure, which draws in cooler air at the lower levels
of the building. If this cooler air is heated (by solar gains, for example) this process will continue. The rate
of airflow caused by this buoyancy is dependent on the height between air intake and outtake, the
temperature difference of the air, and the effective area of the openings (Baker 2015b). The equation for
buoyancy driven flow is given by:

where,
Qs = buoyancy driven ventilation rate (m
3
/s)
Cd = discharge coefficient for opening
Ao = opening area (m
2
)
Fs = opening area fractional schedule
g = gravitational acceleration (9.81 m/s
2
)
Hd = height from the midpoint of lower opening to the midpoint of upper opening (m)
Tin = average indoor temperature between inlet and outlet (K)
Tout = outdoor temperature (K)

Since both wind driven ventilation and buoyancy driven ventilation can occur through the same openings,
they should not be analyzed independently of one another. Honeybee combines the results of the
calculations above with the following equation:

The user inputs for simulating this kind of natural ventilation are: minimum and maximum indoor and outdoor
temperatures, an optional opening area fractional schedule, the fraction of operable glazing area, the
fraction of operable glazing height, coefficients for wind and stack discharge. Maximum and minimum
temperatures are based on analyses in Climate Consultant and on the ASHRAE adaptive thermal comfort
standard for naturally ventilated buildings. The minimum indoor temperature for natural ventilation was set
to 68°F (20°C), as this is the low bound of the comfort zone. Although the upper bound of the comfort zone
is 81°F (27.22°C), 83.6°F (28.67°C) was set as both the maximum indoor and outdoor temperature for
natural ventilation (Brager and de Dear 2000). This is because air that is warmer than the comfort zone
can still cool occupants due to the effects of air movement. The hotter the air is, the faster it must move to
induce a cooling effect. There are limits, of course, as fast moving air is disruptive in most indoor
environments. ASHRAE 55 dictates that appropriate indoor air speeds do not exceed 0.66 fps (0.2 m/s),
except in naturally ventilated spaces, where the allowance goes up to 5.25 fps (1.5 m/s) (ASHRAE and
ANSI 2013). The minimum outdoor temperature was set to 55°F (12.78°C), which is the typical supply air
temperature for conventional air conditioning systems. This value is also outside the comfort zone, but as
this outdoor air will be used to cool the indoor air, it is appropriate that it be lower than what would be a
comfortable ambient air temperature, and the minimum indoor temperature will prevent this air from over-
45

cooling the space. All of the maximum and minimum temperature values represent a conservative annual
value for simulation (Figs. 20 and 21).

Figure 20: Adaptive standard for naturally ventilated buildings
with values for Los Angeles at the red intersections.

Figure 21: Diagram illustrating the maximum and minimum outdoor
allowable temperatures compared to the adaptive thermal comfort
zone.
The fraction of operable glazing area was set to 0.5, meaning that half of the total window area can be
opened. The fraction of operable glazing height was set to 1.0, because the windows are modeled as
double-hung. With this configuration, though only half of the window area can open, that area can be
anywhere along the full height of the window.
The results of the natural ventilation component are then fed into the zone thresholds component. By
assigning the same maximum and minimum temperatures as the heating set point and cooling set point,
46

respectively, a supplemental mechanical system will be activated whenever the temperatures fall outside
of this range. The system simulated is an Ideal Air Loads system, which will supply enough heating or
cooling to meet the zone loads. The output in terms of energy consumption equates to the loads of the
zone; if a real system is required for simulation, coefficients of performance can be used to bring this number
up and take into the account the inefficiencies that effect all real HVAC systems.

Figure 22: Natural ventilation, zone thresholds, and OpenStudio systems components
Essentially, running natural ventilation in this way makes opening the windows a function of temperature.
As a way to validate that the windows were opening when they should (and remaining closed when they
should) a schedule was made showing each hour the windows are open which was graphed against interior
and exterior temperatures (Figs. 23 and 24). This comparison confirms that the natural ventilation is taking
place at the times it is expected and appropriate.

Figure 23: Cold weather natural ventilation schedule for the first week of January. Hours in
grey indicate when the windows are open.
0
1
0
5
10
15
20
25
30
35
40
1
8
15
22
29
36
43
50
57
64
71
78
85
92
99
106
113
120
127
134
141
148
155
162
169
Window (1: open, 0: closed)
Temperature °C
Hour of the Year
First week of January
Window open Outdoor T Indoor T
Int. Low Bound Ext. Low Bound High Bound
47

Figure 24: Hot weather natural ventilation schedule for the first week of August. Hours in
grey indicate when the windows are open.
3.2.4.2 Context
Orientation – A building’s relation to the sun is critical for determining the passive strategies to use on it,
regardless of the building’s age, use, or construction type. Creating an actual case study would generate
useful information about a specific building with a set orientation, which is why a more inclusive
methodology was selected. A simple, representative zone was tested for eight orientations (north,
northwest, west, southwest, south, southeast, east, and northeast) to generate sufficient data points to
estimate the performance of many buildings with different orientations.
A slider was used in Grasshopper to be able to automatically rotate the model 45° at a time. When
connected with the rest of the Honeybee components, eight simulations could be run with a single click,
and the data was written to specially named Excel spreadsheets. This data provides the raw material for
lookup tables and graphs that can be used by owners and designers to quickly estimate the effects of a
particular strategy on energy consumption.
0
1
0
5
10
15
20
25
30
35
40
5088
5095
5102
5109
5116
5123
5130
5137
5144
5151
5158
5165
5172
5179
5186
5193
5200
5207
5214
5221
5228
5235
5242
5249
5256
Window (1: open, 0: closed)
Temperature °C
Hour of the Year
First week of August
Window open Outdoor T Indoor T
Int. Low Bound Ext. Low Bound High Bound
48

Figure 25: Collection of components used to automatically rotate the model during simulation
Window-Wall Ratio – Windows present a number of challenges and opportunities for building designers
and occupants. Because there is no single “right” ratio, multiple WWRs were tested on the same shoebox
model. A slider was used to generate the desired WWR on the wall surface, but this was not automated
as it was for orientation. The WWRs tested were 0.3 (which served as a baseline of sorts because the STB
had a similar WWR), 0.2, 0.4, and 0.5.
3.2.5 Metrics
3.2.5.1 Energy Use Intensity
A common metric used when examining how a building performs in terms of energy consumption is Energy
Use Intensity, or EUI. This figure allows buildings of different sizes to be compared against each other
because consumption is broken down per square foot.
EUI is derived by taking the total annual energy consumption (typically given in kBtu) and dividing by the
total floor area, yielding units of kBtu/sf/yr. This figure can be further broken down by end use to see which
building systems or operations consume more energy. For these simulations, the end uses to assess are
those for cooling, heating, and lighting. Total energy consumption, which includes power used for
equipment, is also used to compare the models against each other.
Honeybee’s Run Energy Simulation component is able to take all of the information defined in the model
and run it through EnergyPlus to calculate the energy consumption based on the schedules and power
densities assigned with the building program (office). However there are other important inputs to
accurately model energy consumption that are not associated with building geometry or default
assumptions. These are input with additional components. After all of the constructions are applied to the
surfaces and the zone is given a program, details about the HVAC system are provided, as discussed in
the section about the natural ventilation variable above. Simulation outputs must be specified; for this case,
all outputs were generated, though only the energy use was necessary.
3.2.5.2 Daylight Autonomy
A collection of Honeybee and Ladybug components were used to determine annual daylighting
performance for the shoebox. Daylight autonomy, developed by Christoph Reinhart and Oliver
Walkenhorst, describes how well a space can be lit with daylight throughout the year, and is the primary
metric used for this analysis (Reinhart and Walkenhorst 2001). It is given as the percentage of occupied
hours over a year that daylight can provide a specified illuminance at a given point. The shoebox model
was divided into approximately 330 points for the analysis and the acceptable minimum illuminance level
49

was set to 300 lux. The simulation determines the lighting level at each point for every hour of the year and
calculates the percentage of occupied hours that a point is above 300 lux. That percentage is then
averaged with the other 329 to yield a value for the entire space.
It is important that, where appropriate, the use of electric lighting be linked to the usable daylight that enters
the space. While this would normally require complex lighting schedules based on indoor illuminance
levels, it can be approximated using an ideal case assumption that electric lighting will only be added to
regions where daylight is insufficient. This approach represents the maximum possible savings if a lighting
technology were to have the granular and dynamic capabilities to harvest 100% of the potential savings.
Since spatial daylight autonomy is the time during the occupied hours that the space is adequately lit, it can
be assumed that the inverse is the time where artificial light is needed. Based on this, the number of
occupied hours requiring electric light is determined and multiplied by the lighting power density of the
space, resulting in an amount of electric light to supplement natural light. The key assumption to this
method is that there is some kind of control to turn off lights when they are not necessary.
3.2.5.3 Thermal Autonomy
The primary driver for the mass adoption of mechanical HVAC systems was the promise of consistent
indoor thermal comfort, therefore it is important that thermal comfort remain a consideration when exploring
various alternatives to mechanical ventilation and space conditioning. An important metric in determining
a zone’s ability to remain comfortable is thermal autonomy (TA). TA was developed by a research team at
Loisos + Ubbelohde, headed by Brendon Levitt. TA links comfort, climate, and the building together in one
metric and is given as a percentage of time the interior temperature lies within a given comfort range. Levitt
defines TA as “the ability for a space to provide acceptable thermal comfort through passive means only”
(Levitt et al. 2013). TA is an innovative and though provoking concept and metric. It looks at any space as
though it were unconditioned and naturally ventilated, truly revealing the issues addressed (or not
addressed) by the architecture itself (Levitt et al. 2013).
Gamas’ custom component was used to determine the thermal autonomy of each test zone. This
component takes the indoor temperature and outdoor temperature and uses those values to assess a range
of hours, either 08 hours to 22 hours, or a full 24 hour cycle (the former was used for this research). The
component uses the following equation when determining the adaptive comfort model:
Tn = 0.31∙Tavg+17.5
where,
Tn = neutrality temperature
Tavg = monthly mean outdoor temperature (daily values of the mean outdoor dry bulb temperature were
used)
Because the component takes the indoor air temperature, the use of mechanical systems has a major effect
on final thermal autonomy values. If the internal temperatures have been affected by mechanical
conditioning, the TA value calculated is, in actuality, a measure of occupant comfort and not reflective of
how well the space can be conditioned passively. In order to get more accurate autonomy values, the BAU
model was simulated as unconditioned and completely sealed. The ER model was simulated as
unconditioned as well, however it utilized natural ventilation through open windows.
3.3 Extrapolation
Building information from the County Assessor helps to create a clearer picture of Los Angeles’s large
building stock, however not all of the necessary information is readily available. 15.96 million square feet
of commercial space (greater than 50,000 square feet per building) built before 1950 is still in use within
the downtown area, but neither the orientation of that square footage, not the WWR associated with all of
50

the walls of these buildings is known. Because of this, city-wide savings were conservatively estimated
based on savings-per-square-foot figures generated from the shoebox simulations.
Though the exact savings figure is difficult to predict, the data collected from the 96 simulations of contextual
variables was used to create charts that illustrate how savings are affected by these difficult-to-change
factors, and provide designers and owners with an easy way to see how their building could change. These
factors can be considered independent variables in the function of energy savings due to passive
intervention.
3.3.1 Zone to Building
The results of each simulation yield an EUI for a zone with glazing of a set WWR that faces a single
direction. Of course, real buildings have glazing on faces that are oriented in numerous directions. To get
an accurate picture of savings possible for a whole building, the building must be divided into sections
based on WWR and orientation. Each zone has a different potential for energy reduction, and they must
be combined to get a final total building savings.
The first step is to divide the building into the appropriate sections based on WWR and orientation. These
sections could be very large, and extend along a building’s entire length. The STB has been divided into
based on how much of its total façade faces a given direction. For the sake of this example, the STB was
made to lie at 45° east of north (rather than the actual grid’s 36°). The building’s total area is listed on the
left for each orientation, as well as the total perimeter. The length of façade facing the given direction is
listed next, which is used to determine a percent of the total area. For example, 500 linear feet of the STB’s
façade face northwest, which is 31.25% of the total 1,600 foot perimeter. This percentage is used to
determine a portion of the total area, to which the reduced EUI is applied.
Table 7: Savings by facade length
Subway Terminal Building
Area
Total
Perimeter
Façade
Length
Orientation WWR
EUI
(kBtu/ft
2
/yr)
kBtu/yr
680,000 1600 500 45 30 151.96
32,291,500.00
680,000 1600 300 135 30 152.64
19,461,600.00
680,000 1600 500 225 30 151.68
32,232,000.00
680,000 1600 300 315 30 150.91
19,241,025.00
Total: 103,226,125.00
This is compared to an analysis of the entire floorplate of the STB, conducted with the same Honeybee and
Ladybug components used to simulate the BAU and ER models.
3.3.2 Zone to Building Stock
Determining the exact potential energy reduction across all of Los Angeles’ building stock is exceptionally
difficult for two reasons. First, the built environment in Los Angeles is vast. 15,962,234 square feet of
commercial space (greater than 50,000 square feet) built before 1950 is still in existence. Second, the
savings for each building in this square footage will be different, based on the WWR and orientations that
help define the building.
Luckily, the values for the entire building stock do not need to be exact, as these figures are most useful to
planners and other city officials rather than the building owners themselves. First order estimates for energy
reduction can be made using the combined reduction factors that were calculated for each orientation. This
is done by averaging the values together for each of the orientations, based on estimations for how much
of the building stock is oriented in a particular way. Essentially, since each building (likely) has four sides,
51

the question becomes how much of the building stock is oriented along the Jeffersonian grid and how much
of it along the tilted grid. Ultimately, this detracts from the nuances of orientation and WWR that were
calculated in each of the simulation comparisons, however it is a useful and simple figure for evaluating the
pre-war buildings of Los Angeles as a whole.
Table 8: Values for determining building stock energy reduction

kBtu/ft
2
/yr
Reduction
BAU ER
TOTAL AVERAGES: 216.32 153.45 -29.06%

Table 9: Energy consumed by pre-war commercial
buildings (>50,000 ft
2
) in downtown Los Angeles

Annual Energy Consumption
(kBtu)
BAU 3,452,950,459
ER 2,449,523,056
52

4 Results
The research objective of this thesis was to determine the effectiveness of reintegrating passive strategies
in historic, pre-war buildings in downtown Los Angeles. The city needs to reduce the energy consumption
of existing buildings by 40% to meet the carbon emissions goals mandated by the State of California. The
hypothesis of this research is that by re-implementing the passive strategies in these buildings, an energy
use reduction of at least 40% is possible.
This chapter presents the results of the 96 simulations conducted on the shoebox model to determine the
potential for energy reduction. The simulations were used to determine total energy consumption, energy
consumption by end use, energy use intensity (EUI), thermal autonomy, and daylight autonomy. The
results are grouped by the respective window-to-wall ratio (WWR) of each case.
4.1 Summary of Results
The results presented in this section consist of aggregations of the simulations run for each WWR. For
each case, the environmentally responsive (ER) model has been compared against the business-as-usual
(BAU) model with the same WWR. The WWR for each model will be denoted as a subscript, for example,
BAU30 is compared to ER30.
The primary goal of this research was to determine the potential savings that could be realized by
implementing passive strategies in pre-war buildings that had been previously retrofit to be completely
dependent on mechanical space conditioning and ventilation. The total energy savings against each
respective baseline is illustrated with the chart below (Fig. 26).

Figure 26: Potential energy reduction of ER model over BAU model for given WWRs and orientations.
0
5
10
15
20
25
30
35
40
0 45 90 135 180 225 270 315 360
Percent Total Energy Saved
Degrees counter-clockwise from north (0°)
Percent energy reduction of ER model compared to BAU model for 4 WWRs
WWR-20 WWR-30 WWR-40 WWR-50
53

The simulations showed that the highest potential for savings lies with buildings oriented toward the south
(180° on the chart above). It is also clear that zones with the WWR of 0.5 have greater savings potential
(though the difference between WWR40 and WWR50 is almost indistinguishable. This distribution of potential
savings is made clear with the heat map table (Table 10). The actual savings in kBtu/ft
2
/yr (Fig. 27) illustrate
the same pattern: the percent of potential savings decreases as WWR decreases.
Table 10: The potential savings in percent for given WWRs and orientations.
High values (greater than 30%) are shown in red and low values (less than
27%) are shown in green.
Orientation WWR-20 WWR-30 WWR-40 WWR-50
0 22.54 29.21 30.55 30.79
45 21.86 28.94 30.48 30.64
90 21.52 29.56 31.38 31.58
135 22.16 30.66 32.52 32.74
180 23.18 31.72 33.65 33.93
225 22.99 31.2 32.96 33.29
270 22.69 30.28 31.91 32.26
315 21.09 28.58 30.41 30.77
360 22.54 29.21 30.55 30.79

Figure 27: Average of actual savings for all orientations with a given WWR.
Understanding the breakdown of end-use energy is an important part of determining where a building can
reduce energy consumption. For all of the simulations run in this research, cooling was the single largest
energy end use, followed by equipment (computers, copiers, microwaves, refrigerators, etc.) and lighting.
Heating, in comparison, was only a tiny portion of total energy consumption, (though it did rise significantly
within the ER models). The graphs presented below illustrate the EUI by end-use for each simulation.
The first set of graphs displays information for cooling (Figs. 28 and 29). For each simulation, the model
oriented to 135° from north (southwest) has the highest cooling loads because of the combined southern
exposure and setting sun. In each grouping by WWR, it is clear that orientation does have an effect on
energy consumption, and the data indicate that as the WWR increases, that effect becomes greater. The
shape of the graphs for both BAU and ER models are similar, except for the slight dip at the 180° orientation
for the ER simulations. For more discussion on the effects of WWR and orientation on cooling energy for
the two models, see chapter 5, section 5.3.
47.2
64.52
69.12
70.65
0
10
20
30
40
50
60
70
80
WWR-20 WWR-30 WWR-40 WWR-50
kBtu/fr
2
/yr
Averaged savings against baseline in kBtu/ft
2
/yr for each WWR
54

Figure 28: Cooling energy use in BAU model

Figure 29: Cooling energy use in BAU model
WWR-20 WWR-30 WWR-40 WWR-50
0 119.53 120.25 120.98 121.7
45 120.77 122.38 124.00 125.63
90 122.73 125.96 129.24 132.54
135 124.59 128.65 132.8 137.01
180 124.13 127.88 131.71 135.59
225 123.58 127.00 130.41 133.87
270 121.39 123.82 126.29 128.72
315 120.04 121.21 122.4 123.59
40
50
60
70
80
90
100
110
120
130
140
kBtu/ft
2
/yr
Cooling energy use in BAU model
0 45 90 135 180 225 270 315
WWR-20 WWR-30 WWR-40 WWR-50
0 47.93 52.11 55.95 59.42
45 48.42 53.24 57.87 62.17
90 48.43 54.00 59.68 65.31
135 48.82 54.48 60.46 66.59
180 48.00 52.95 58.16 63.58
225 48.32 53.34 58.52 63.62
270 47.83 52.65 57.41 61.96
315 47.88 52.25 56.38 60.13
40.00
50.00
60.00
70.00
80.00
90.00
100.00
110.00
120.00
130.00
140.00
kBtu/ft
2
/yr
Cooling energy use in ER model
0 45 90 135 180 225 270 315
55

While cooling energy is the highest, heating energy is the lowest for the BAU model (Fig. 30) which is
appropriate considering the climate of downtown Los Angeles. Heating loads rise in the ER model’s lowest
WWRs due to the loss of needed winter solar gains (Fig. 31). Cooling energy peaks at the southern
orientations, but heating energy peaks at the northern orientations, with the highest heating loads at 0° from
north for the BAU models. The lowest heating energy occurs at 225° from north (SE) because of the
southern exposure and rising sun in the eastern sky. The ER model’s west and north faces experience the
greatest heat loads because they don’t have the benefit of the rising sun to warm the building before and
in the early stages of daily occupancy.

Figure 30: Heating energy use in BAU model

Figure 31: Heating energy use in ER model
WWR-20 WWR-30 WWR-40 WWR-50
0 5.03 5.33 5.65 5.99
45 4.99 5.29 5.62 5.97
90 4.72 5.02 5.35 5.69
135 4.35 4.66 4.98 5.32
180 4.09 4.33 4.59 4.88
225 4.04 4.16 4.31 4.5
270 4.34 4.42 4.56 4.74
315 4.84 5.04 5.28 5.54
0
5
10
15
20
25
30
35
kBtu/ft
2
/yr
Heating energy use in BAU model
0 45 90 135 180 225 270 315
WWR-20 WWR-30 WWR-40 WWR-50
0 29.30 16.72 11.59 9.27
45 31.16 17.81 12.30 9.75
90 33.20 18.73 12.66 9.78
135 32.62 18.05 12.08 9.27
180 30.68 16.53 10.90 8.30
225 30.32 16.31 10.58 7.95
270 29.99 16.40 10.80 8.23
315 32.62 18.52 12.62 9.79
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
kBtu/ft
2
/yr
Heating energy use in ER model
0 45 90 135 180 225 270 315
56

Lighting energy is calculated in EnergyPlus based on a lighting load per square foot. Since total square
footage does not change, the lighting loads for every BAU run is constant at 13.25 kBtu/ft
2
/yr. The lighting
loads for ER models were calculated based on the inverse of the daylight autonomy figure, discussed later
in this chapter. The energy consumed for lighting mirrors that of heating, where the highest consumption
happens at northern orientation and the lowest at the southern orientations (Fig. 32).

Figure 32: Lighting energy use in ER model
Energy consumption for equipment is also based on power per square foot. These process loads do not
change between models, nor among WWRs or orientations, so they are not displayed like the other end-
uses. However, the data is displayed in the granular results later in this chapter,
Daylight and thermal autonomies were important metrics in this research. Thermal autonomy is a measure
of how much a space can be comfortable without the intervention of mechanical systems. Like daylight
autonomy, it is also given as a percentage of occupied hours that the space is in a given comfort range. It
is important to note what is measured when seeing the results for thermal autonomy. Ideally, thermal
autonomy is measured for unconditioned spaces (or measured as if the space was unconditioned),
otherwise it would simply be an indicator of the percent of the time that the system keeps the space
comfortable. To show how the space harnesses natural forces, the BAU model was simulated as
unconditioned and the ER model was simulated as naturally ventilated (Fig. 33). Included with the actual
thermal autonomy figures are charts that display the comfort values for the conditioned spaces (Figs. 34
and 35).

WWR-20 WWR-30 WWR-40 WWR-50
0 9.83 8.35 7.55 6.62
45 9.63 8.19 7.15 6.42
90 8.87 7.51 6.46 5.74
135 8.23 6.94 5.9 5.14
180 8.19 6.94 5.86 5.18
225 8.51 7.18 6.3 5.54
270 9.11 7.75 6.86 5.9
315 9.63 8.23 7.11 6.42
BAU 13.25 13.25 13.25 13.25
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
kBtu/ft
2
/yr
Lighting energy use in ER model
57

Figure 33: Thermal autonomy for sealed and naturally ventilated zones

Figure 34: Percent of time comfortable in BAU model
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
0 45 90 135 180 225 270 315 360
Thermal Autonomy (%)
Thermal autonomy of sealed (BAU) and naturally ventilated
(ER) models
Sealed, unconditioned WWR-20 Sealed, unconditioned WWR-30
Sealed, unconditioned WWR-40 Sealed, unconditioned WWR-50
Naturally Ventilated WWR-20 Naturally Ventilated WWR-30
Naturally Ventilated WWR-40 Naturally Ventilated WWR-50
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
0 45 90 135 180 225 270 315 360
Time Comfortable (%)
Percent of time comfortable in BAU model at given orientations
WWR-20 WWR-30 WWR-40 WWR-50
58

Figure 35: Percent of occupied hours comfortable in ER model
Daylight autonomy is a measure of how well a space is daylit. It is given as a percentage of time that a
space is daylight over a specified lighting level for occupied hours. Refer to section 3.2.5.2 for more details.
Below, the positive relationship of WWR to daylight autonomy is illustrated, as well as the increased
autonomy with southern exposures (Fig. 36).

Figure 36: Daylight autonomy for 4 WWRs
4.2 Granular Results
The following sections detail the individual simulations by WWR. The four WWRs tested were 20%, 30%,
40% and 50%. Each is further broken down by simulation engine and the associated outcomes, comparing
BAU models and ER models side-by-side, where appropriate.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
0 45 90 135 180 225 270 315 360
Time Comfortable (%)
Percent of time comfortable in ER model at given orientations
WWR-20 WWR-30 WWR-40 WWR-50
0%
10%
20%
30%
40%
50%
60%
70%
0 45 90 135 180 225 270 315 360
Daylight autonomy for 4 WWRs at given orientations
WWR-20 WWR-30 WWR-40 WWR-50
59

4.2.1 WWR-20
The BAU model represents the zone in an as-is condition. It utilizes conventional cooling and heating
strategies (mechanical systems) and has a sealed envelope. The ER model uses the same geometry as
the BAU model, but utilizes mixed-mode technology to condition the space. This means that the windows
are able to open to let outdoor air inside when appropriate, and a supplementary system is used when the
outdoor air is unsuitable. For both models, the WWR was set to 20%. A detailed explanation of each model
can be found in Chapter 3.
4.2.1.1 Results from EnergyPlus
EnergyPlus was used to determine the energy consumption of the shoebox zone in both BAU and ER
configurations. The following tables display the two zones’ energy consumptions, and the savings
calculated from BAU to ER for each of the eight orientations simulated. Notice that the lighting load for the
BAU model and the equipment loads for both models do not change, because they are based on loads per
square foot and are not affected by changes in orientation or WWR.
Table 11: Energy consumption by end use in BAU0 and
ER0 models in kBtu/ft
2
/yr
0° (N) BAU ER Savings
Cooling 119.53 47.93 -59.90%
Heating 5.03 29.30 482.20%
Lighting 13.25 9.83 -25.79%
Equipment 72.20 72.20 0.00%
Total 210.01 162.67 -22.54%

Table 12: Energy consumption by end use in BAU45
and ER45 models in kBtu/ft
2
/yr
45° (NW) BAU ER Savings
Cooling 120.77 48.42 -59.91%
Heating 4.99 31.16 525.15%
Lighting 13.25 9.63 -27.30%
Equipment 72.20 72.20 0.00%
Total 211.20 165.03 -21.86%

Table 13: Energy consumption by end use in BAU90
and ER90 models in kBtu/ft
2
/yr
90° (W) BAU ER Savings
Cooling 122.73 48.43 -60.54%
Heating 4.72 33.20 603.82%
Lighting 13.25 8.87 -33.04%
Equipment 72.20 72.20 0.00%
Total 212.89 167.07 -21.52%
Table 14: Energy consumption by end use in BAU135
and ER135 models in kBtu/ft
2
/yr
135° (SW)

BAU ER Savings
Cooling

124.59 48.82 -60.82%
Heating

4.35 32.62 649.43%
Lighting

13.25 8.23 -37.87%
Equipment

72.20 72.20 0.00%
Total

214.39
166.88 -22.16%

Table 15: Energy consumption by end use in BAU180
and ER180 models in kBtu/ft
2
/yr
180° (S) BAU ER Savings
Cooling 124.13 48.00 -61.33%
Heating 4.09 30.68 649.62%
Lighting 13.25 8.19 -38.17%
Equipment 72.20 72.20 0.00%
Total 213.67 164.13 -23.18%

Table 16: Energy consumption by end use in BAU225
and ER225 models in kBtu/ft
2
/yr
225° (SE) BAU ER Savings
Cooling 123.58 48.32 -60.90%
Heating 4.04 30.32 650.50%
Lighting 13.25 8.51 -35.76%
Equipment 72.20 72.20 0.00%
Total 213.07 164.08 -22.99%
60

Table 17: Energy consumption by end use in BAU270
and ER270 models in kBtu/ft
2
/yr
270° (E) BAU ER Savings
Cooling 121.39 47.83 -60.60%
Heating 4.34 29.99 591.69%
Lighting 13.25 9.11 -31.23%
Equipment 72.20 72.20 0.00%
Total 211.17 163.26 -22.69%

Table 18: Energy consumption by end use in BAU315
and ER315 models in kBtu/ft
2
/yr
315° (NE) BAU ER Savings
Cooling 120.04 47.88 -60.11%
Heating 4.84 32.62 574.54%
Lighting 13.25 9.63 -27.30%
Equipment 72.20 72.20 0.00%
Total 210.32 165.95 -21.09%
Pure energy consumption data is not sufficient for determining the success of the ER strategies over BAU
strategies. Comfort and thermal autonomy data are necessary to illustrate that energy consumption is
reduced without greatly affecting the building occupants in a negative way. The following figures graphically
illustrate the thermal autonomy for each configuration and orientation at WWR-20. The different
configurations are: 1) sealed (unconditioned), which is a measure of thermal autonomy for the BAU model;
2) natural ventilation (unconditioned), which is a measure of thermal autonomy for the ER model; 3) base,
which shows comfort in the BAU model; and 4) mixed-mode, which shows comfort in the ER model. The
following graphs compare highs and lows for each of the ER models.
61

Figure 37: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the highest
energy-consuming orientation (45°) for WWR-20
62

Figure 38: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the lowest
energy-consuming orientation (180°) for WWR-20

63

4.2.1.2 Results from Radiance
Radiance was used to conduct daylighting analysis of the shoebox model. Though lighting is ultimately
included in the results with energy, this daylighting analysis was necessary to determine how much natural
light could reduce the artificial light, therefore leading to energy savings. Because of the direct relationship
between daylight autonomy and the energy saved, the percent savings for lighting in the tables in the
previous section is the same as the daylight autonomy. As DA is not affected by the conditioning strategies
used, it was not necessary to conduct separate simulations for BAU and ER models. The following figures
show the DA mapped in the zone for each orientation.

4.2.2 WWR-30
The BAU model represents the zone in an as-is condition. It utilizes conventional cooling and heating
strategies (mechanical systems) and has a sealed envelope. The ER model uses the same geometry as
the BAU model, but utilizes mixed-mode technology to condition the space. This means that the windows
are able to open to let outdoor air inside when appropriate, and a supplementary system is used when the
outdoor air is unsuitable. For both models, the WWR was set to 30%. A detailed explanation of each model
can be found in Chapter 3.
64

4.2.2.1 Results from EnergyPlus
EnergyPlus was used to determine the energy consumption of the shoebox zone in both BAU and ER
configurations. The following tables display the two zones’ energy consumptions, and the savings
calculated from BAU to ER for each of the eight orientations simulated. Notice that the lighting load for the
BAU model and the equipment loads for both models do not change, because they are based on loads per
square foot and are not affected by changes in orientation or WWR.
Table 19: Energy consumption by end use in BAU0 and
ER0 models in kBtu/ft
2
/yr
0° (N) BAU ER Savings
Cooling 120.25 52.11 -56.66%
Heating 5.33 16.72 213.49%
Lighting 13.25 8.35 -36.97%
Equipment 72.20 72.20 0.00%
Total 211.03 149.38 -29.21%

Table 20: Energy consumption by end use in BAU45
and ER45 models in kBtu/ft
2
/yr
45° (NW) BAU ER Savings
Cooling 122.38 53.24 -56.50%
Heating 5.29 17.81 236.60%
Lighting 13.25 8.19 -38.18%
Equipment 72.20 72.20 0.00%
Total 213.12 151.44 -28.94%

Table 21: Energy consumption by end use in BAU90
and ER90 models in kBtu/ft
2
/yr
90° (W) BAU ER Savings
Cooling 125.96 54.00 -57.13%
Heating 5.02 18.73 273.31%
Lighting 13.25 7.51 -43.33%
Equipment 72.20 72.20 0.00%
Total 216.42 152.44 -29.56%

Table 22: Energy consumption by end use in BAU135
and ER135 models in kBtu/ft
2
/yr
135° (SW) BAU ER Savings
Cooling 128.65 54.48 -57.65%
Heating 4.66 18.05 287.35%
Lighting 13.25 6.94 -47.58%
Equipment 72.20 72.20 0.00%
Total 218.75 151.67 -30.66%

Table 23: Energy consumption by end use in BAU180
and ER180 models in kBtu/ft
2
/yr
180° (S) BAU ER Savings
Cooling 127.88 52.95 -58.59%
Heating 4.33 16.53 281.42%
Lighting 13.25 6.94 -47.58%
Equipment 72.20 72.20 0.00%
Total 217.66 148.62 -31.72%

Table 24: Energy consumption by end use in BAU225
and ER225 models in kBtu/ft
2
/yr
225° (SE) BAU ER Savings
Cooling 127.00 53.34 -58.00%
Heating 4.16 16.31 291.85%
Lighting 13.25 7.18 -45.76%
Equipment 72.20 72.20 0.00%
Total 216.61 149.04 -31.20%

Table 25: Energy consumption by end use in BAU270
and ER270 models in kBtu/ft
2
/yr
270° (E) BAU ER Savings
Cooling 123.82 52.65 -57.48%
Heating 4.42 16.40 270.65%
Lighting 13.25 7.75 -41.52%
Equipment 72.20 72.20 0.00%
Total 213.68 148.99 -30.28%

Table 26: Energy consumption by end use in BAU315
and ER315 models in kBtu/ft
2
/yr
315° (NE) BAU ER Savings
Cooling 121.21 52.25 -56.89%
Heating 5.04 18.52 267.39%
Lighting 13.25 8.23 -37.88%
Equipment 72.20 72.20 0.00%
Total 211.70 151.20 -28.58%
Pure energy consumption data is not sufficient for determining the success of the ER strategies over BAU
strategies. Comfort and thermal autonomy data are necessary to illustrate that energy consumption is
reduced without greatly affecting the building occupants in a negative way. The following figures graphically
65

illustrate the thermal autonomy for each configuration and orientation at WWR-30. The different
configurations are: 1) sealed (unconditioned), which is a measure of thermal autonomy for the BAU model;
2) natural ventilation (unconditioned), which is a measure of thermal autonomy for the ER model; 3) base,
which shows comfort in the BAU model; and 4) mixed-mode, which shows comfort in the ER model.
66

Figure 39: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the highest
energy-consuming orientation (135°) for WWR-30
67

Figure 40: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the lowest
energy-consuming orientation (315°) for WWR-30

68

4.2.2.2 Results from Radiance
Radiance was used to conduct daylighting analysis of the shoebox model. Though lighting is ultimately
included in the results with energy, this daylighting analysis was necessary to determine how much natural
light could reduce the artificial light, therefore leading to energy savings. Because of the direct relationship
between daylight autonomy and the energy saved, the percent savings for lighting in the tables in the
previous section is the same as the daylight autonomy. As DA is not affected by the conditioning strategies
used, it was not necessary to conduct separate simulations for BAU and ER models. The following figures
show the DA mapped in the zone for each orientation.

4.2.3 WWR-40
The BAU model represents the zone in an as-is condition. It utilizes conventional cooling and heating
strategies (mechanical systems) and has a sealed envelope. The ER model uses the same geometry as
the BAU model, but utilizes mixed-mode technology to condition the space. This means that the windows
69

are able to open to let outdoor air inside when appropriate, and a supplementary system is used when the
outdoor air is unsuitable. For both models, the WWR was set to 40%. A detailed explanation of each model
can be found in Chapter 3.
4.2.3.1 Results from EnergyPlus
EnergyPlus was used to determine the energy consumption of the shoebox zone in both BAU and ER
configurations. The following tables display the two zones’ energy consumptions, and the savings
calculated from BAU to ER for each of the eight orientations simulated. Notice that the lighting load for the
BAU model and the equipment loads for both models do not change, because they are based on loads per
square foot and are not affected by changes in orientation or WWR.
Table 27: Energy consumption by end use in BAU0 and
ER0 models in kBtu/ft
2
/yr
0° (N) BAU ER Savings
Cooling 120.98 55.95 -53.75%
Heating 5.65 11.59 105.04%
Lighting 13.25 7.55 -43.03%
Equipment 72.20 72.20 0.00%
Total 212.08 147.29 -30.55%

Table 28: Energy consumption by end use in BAU45
and ER45 models in kBtu/ft
2
/yr
45° (NW) BAU ER Savings
Cooling 124.00 57.87
-53.33%
Heating 5.62
12.30
118.66%
Lighting 13.25 7.15 -46.06%
Equipment 72.20 72.20 0.00%
Total 215.07
149.51
-30.48%

Table 29: Energy consumption by end use in BAU90
and ER90 models in kBtu/ft
2
/yr
90° (W) BAU ER Savings
Cooling 129.24 59.68 -53.82%
Heating 5.35 12.66 136.52%
Lighting 13.25 6.46 -51.21%
Equipment 72.20 72.20 0.00%
Total 220.03 151.00 -31.38%

Table 30: Energy consumption by end use in BAU135
and ER135 models in kBtu/ft
2
/yr
135° (SW) BAU ER Savings
Cooling 132.80 60.46 -54.47%
Heating 4.98 12.08 142.45%
Lighting 13.25 5.90 -55.45%
Equipment 72.20 72.20 0.00%
Total 223.23 150.64 -32.52%

Table 31: Energy consumption by end use in BAU180
and ER180 models in kBtu/ft
2
/yr
180° (S) BAU ER Savings
Cooling 131.71 58.16 -55.84%
Heating 4.59 10.90 137.51%
Lighting 13.25 5.86 -55.76%
Equipment 72.20 72.20 0.00%
Total 221.74 147.12 -33.65%

Table 32: Energy consumption by end use in BAU225
and ER225 models in kBtu/ft
2
/yr
225° (SE) BAU ER Savings
Cooling 130.41 58.52 -55.13%
Heating 4.31 10.58 145.44%
Lighting 13.25 6.30 -52.42%
Equipment 72.20 72.20 0.00%
Total 220.17 147.59 -32.96%

Table 33: Energy consumption by end use in BAU270
and ER270 models in kBtu/ft
2
/yr
270° (E) BAU ER Savings
Cooling 126.29 57.41 -54.54%
Heating 4.56 10.80 136.99%
Lighting 13.25 6.86 -48.18%
Equipment 72.20 72.20 0.00%
Total 216.29 147.27 -31.91%

Table 34: Energy consumption by end use in BAU315
and ER315 models in kBtu/ft
2
/yr
315° (NE) BAU ER Savings
Cooling 122.40 56.38 -53.94%
Heating 5.28 12.62 139.14%
Lighting 13.25 7.11 -46.36%
Equipment 72.20 72.20 0.00%
Total 213.13 148.30 -30.41%
70

Pure energy consumption data is not sufficient for determining the success of the ER strategies over BAU
strategies. Comfort and thermal autonomy data are necessary to illustrate that energy consumption is
reduced without greatly affecting the building occupants in a negative way. The following figures graphically
illustrate the thermal autonomy for each configuration and orientation at WWR-40. The different
configurations are: 1) sealed (unconditioned), which is a measure of thermal autonomy for the BAU model;
2) natural ventilation (unconditioned), which is a measure of thermal autonomy for the ER model; 3) base,
which shows comfort in the BAU model; and 4) mixed-mode, which shows comfort in the ER model.
71

Figure 41: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the highest
energy-consuming orientation (135°) for WWR-40
72

Figure 42: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the lowest
energy-consuming orientation (0°) for WWR-40

73

4.2.3.2 Results from Radiance
Radiance was used to conduct daylighting analysis of the shoebox model. Though lighting is ultimately
included in the results with energy, this daylighting analysis was necessary to determine how much natural
light could reduce the artificial light, therefore leading to energy savings. Because of the direct relationship
between daylight autonomy and the energy saved, the percent savings for lighting in the tables in the
previous section is the same as the daylight autonomy. As DA is not affected by the conditioning strategies
used, it was not necessary to conduct separate simulations for BAU and ER models. The following figures
show the DA mapped in the zone for each orientation.

4.2.4 WWR-50
The BAU model represents the zone in an as-is condition. It utilizes conventional cooling and heating
strategies (mechanical systems) and has a sealed envelope. The ER model uses the same geometry as
74

the BAU model, but utilizes mixed-mode technology to condition the space. This means that the windows
are able to open to let outdoor air inside when appropriate, and a supplementary system is used when the
outdoor air is unsuitable. For both models, the WWR was set to 50%. A detailed explanation of each model
can be found in Chapter 3.
4.2.4.1 Results from EnergyPlus
EnergyPlus was used to determine the energy consumption of the shoebox zone in both BAU and ER
configurations. The following tables display the two zones’ energy consumptions, and the savings
calculated from BAU to ER for each of the eight orientations simulated. Notice that the lighting load for the
BAU model and the equipment loads for both models do not change, because they are based on loads per
square foot and are not affected by changes in orientation or WWR.
Table 35: Energy consumption by end use in BAU0 and
ER0 models in kBtu/ft
2
/yr
0° (N) BAU ER Savings
Cooling 121.70 59.42 -51.18%
Heating 5.99 9.27 54.69%
Lighting 13.25 6.62 -50.00%
Equipment 72.20 72.20 0.00%
Total 213.14 147.51 -30.79%

Table 36: Energy consumption by end use in BAU45
and ER45 models in kBtu/ft
2
/yr
45° (NW) BAU ER Savings
Cooling 125.63 62.17 -50.51%
Heating 5.97 9.75 63.38%
Lighting 13.25 6.42 -51.52%
Equipment 72.20 72.20 0.00%
Total 217.05 150.55 -30.64%

Table 37: Energy consumption by end use in BAU90
and ER90 models in kBtu/ft
2
/yr
90° (W) BAU ER Savings
Cooling 132.54 65.31 -50.73%
Heating 5.69 9.78 72.02%
Lighting 13.25 5.74 -56.67%
Equipment 72.20 72.20 0.00%
Total 223.67 153.03 -31.58%

Table 38: Energy consumption by end use in BAU135
and ER135 models in kBtu/ft
2
/yr
135° (SW) BAU ER Savings
Cooling 137.01 66.59 -51.40%
Heating 5.32 9.27 74.19%
Lighting 13.25 5.14 -61.21%
Equipment 72.20 72.20 0.00%
Total 227.77 153.19 -32.74%
Table 39: Energy consumption by end use in BAU180
and ER180 models in kBtu/ft
2
/yr
180° (S) BAU ER Savings
Cooling 135.59 63.58 -53.11%
Heating 4.88 8.30 70.08%
Lighting 13.25 5.18 -60.91%
Equipment 72.20 72.20 0.00%
Total 225.92 149.26 -33.93%

Table 40: Energy consumption by end use in BAU225
and ER225 models in kBtu/ft
2
/yr
225° (SE) BAU ER Savings
Cooling 133.87 63.62 -52.48%
Heating 4.50 7.95 76.60%
Lighting 13.25 5.54 -58.18%
Equipment 72.20 72.20 0.00%
Total 223.82 149.31 -33.29%

Table 41: Energy consumption by end use in BAU270
and ER270 models in kBtu/ft
2
/yr
270° (E) BAU ER Savings
Cooling 128.72 61.96 -51.87%
Heating 4.74 8.23 73.81%
Lighting 13.25 5.90 -55.45%
Equipment 72.20 72.20 0.00%
Total 218.90 148.29 -32.26%

Table 42: Energy consumption by end use in BAU315
and ER315 models in kBtu/ft
2
/yr
315° (NE) BAU ER Savings
Cooling 123.59 60.13 -51.34%
Heating 5.54 9.79 76.64%
Lighting 13.25 6.42 -51.52%
Equipment 72.20 72.20 0.00%
Total 214.58 148.54 -30.77%
75

Pure energy consumption data is not sufficient for determining the success of the ER strategies over BAU
strategies. Comfort and thermal autonomy data are necessary to illustrate that energy consumption is
reduced without greatly affecting the building occupants in a negative way. The following figures graphically
illustrate the thermal autonomy for each configuration and orientation at WWR-50. The different
configurations are: 1) sealed (unconditioned), which is a measure of thermal autonomy for the BAU model;
2) natural ventilation (unconditioned), which is a measure of thermal autonomy for the ER model; 3) base,
which shows comfort in the BAU model; and 4) mixed-mode, which shows comfort in the ER model.
76

Figure 43: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the highest
energy-consuming orientation (135°) for WWR-50
77

Figure 44: Thermal Comfort (top two graphs) and thermal autonomy (bottom two graphs) for the lowest
energy-consuming orientation (0°) for WWR-50

78

4.2.4.2 Results from Radiance
Radiance was used to conduct daylighting analysis of the shoebox model. Though lighting is ultimately
included in the results with energy, this daylighting analysis was necessary to determine how much natural
light could reduce the artificial light, therefore leading to energy savings. Because of the direct relationship
between daylight autonomy and the energy saved, the percent savings for lighting in the tables in the
previous section is the same as the daylight autonomy. As DA is not affected by the conditioning strategies
used, it was not necessary to conduct separate simulations for BAU and ER models. The following figures
show the DA mapped in the zone for each orientation.

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5 Discussion
5.1 Summary
The ultimate goal of this research was to determine whether or not the reintegration of passive daylighting
and cooling strategies in historic pre-war buildings would be sufficient to help Los Angeles meet its state-
mandated carbon emission goals. To be on track to cut carbon emissions to 80% below 1990 levels by
2050, all existing buildings in Los Angeles need to reduce their energy consumption by 40%. The
hypothesis of this research was that the simulated strategies would be sufficient to result in this 40%
reduction. However, the results show that these strategies on their own fall short of that target, at 30%.
Even so, this is a sizeable reduction and the methodology developed and data gathered can provide
valuable insights to city officials and building owners.
5.2 The Benefits of Environmentally Responsive Construction
The environmentally responsive (ER) model shows significant reductions for energy consumption used for
cooling, heating, and lighting for every orientation and WWR. The benefits of these reductions are many,
and address the values of different shareholders. The reduction in energy consumption across LA’s pre-
war commercial building stock alone could translate to 202,791 tons of CO2 averted each year.
There are financial benefits to these savings as well. Research from Carnegie Mellon University suggests
that the addition of a mixed-mode system could lead to productivity increases ranging from 3% to 18% and
have an ROI of 120% over the course of the first year due to energy savings and enhancements in worker
productivity and health (Center for Building Performance and Diagnostics 2004). Based on the average
price per kilowatt in Los Angeles (21.5¢), the reintegration of the passive strategies simulated could
potentially save $63,523,684 per year for the 15.96 million square feet of commercial buildings downtown.
Considering that the cost of a mixed-mode retrofit is estimated to be $17 per square foot, the savings of
$3.98 per square foot will pay off the investment relatively quickly (Center for Building Performance and
Diagnostics 2004).
These retrofits to reintroduce passive strategies are also useful because they enhance the building’s
passive survivability. Passive survivability is a building’s ability to maintain livable (though not necessarily
comfortable) conditions in the event of an extended loss of power (NJ Green Building Manual 2011). In
this climate zone (CA climate zone 9) extreme heat events are of particular concern. The demand for
electricity spikes during extreme heat events, but if there is not enough power to meet demand, building
occupants have no choice but to make due with less, or even no power. In a sealed building, the inability
to open windows causes indoor temperatures to rise to unsafe levels, leading to an evacuation of the
building and incredible losses in productivity. A building that is naturally ventilated, however, is able to
avoid the unsafe temperatures by being opened and allowing for natural ventilation. The charts below
illustrate the difference in thermal autonomy of a sealed building without power and a naturally ventilated
building without power (Fig. 45). Clearly, the sealed building experiences greater thermal discomfort for
greater portions of the year.
80

Figure 45: Top: the sealed building simulated without mechanical conditioning systems; Bottom: the same zone with
natural ventilation and no mechanical conditioning.
5.3 Discussion of Variables
The simulations were conducted using intervention variables and contextual variables. The intervention
variables represent the passive strategies that could be re-implemented in pre-war buildings. Specifically,
these variables were the use of mixed-mode ventilation and the use of photosensor-controlled lighting
systems. Contextual variables represent the building characteristics that are not readily changed, and
descriptions of each variable and its calculation methods can be found in chapter 3.
5.3.1 Mixed-mode Ventilation
Mixed-mode systems offer a hybrid approach to space cooling, utilizing natural ventilation when the climate
allows it, and supplementing that natural (and free) cooling with mechanical systems when air temperatures
are no longer conducive to thermal comfort. This can result in significant energy consumption reductions
for cooling, but its success is affected by the zone’s orientation and glazing ratios. In the ER cases
simulated for this research, cooling loads were reduced by an average of 55% with the introduction of
mixed-mode systems. Not only was this a sizable reduction as a percent of the BAU case, the actual kBtus
saved due to this intervention were higher than any other end-use (Fig. 46). This illustrates mixed-mode
systems as the primary driver for energy reduction in pre-war buildings in Los Angeles.
81

Figure 46: Energy consumption by end use, grouped as BAU and ER by WWR
5.3.1.1 Effects of Orientation and WWR
The effects of a zone’s orientation on cooling energy is clear: as the orientation becomes more southward,
the energy required to cool the zone increases. This is illustrated with the case BAU30, where the lowest
cooling load is seen at 0° from north (due north) with 120.25 kBtu/ft
2
/yr and the highest cooling load occurs
at 135° from north (southwest) with 128.65 kBtu/ft
2
/yr (Fig. 47).

Figure 47: Cooling energy by WWR and orientation
0.00
50.00
100.00
150.00
200.00
250.00
BAU ER BAU ER BAU ER BAU ER
WWR-20 WWR-30 WWR-40 WWR-50
kBtu/ft
2
/yr
Energy consumption by end use
Cooling Heating Lighting Equipment
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
BAU ER BAU ER BAU ER BAU ER
WWR-20 WWR-30 WWR-40 WWR-50
kBtu/ft
2
/yr
Cooling energy by WWR and orientation
0 45 90 135 180 225 270 315
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It is not surprising that the southwest orientation requires has a higher cooling load than the orientation due
south. This is because of the effects of the setting sun. A southern and southwestern exposure both
experience considerable lengths of the day with high levels of direct solar radiation due to the sun’s position
in the southern sky. However, only a southwestern orientation experiences this intense southern sun in
addition to the radiation from the setting sun in the west (Fig 48). Because the southern exposure has been
heated all day, the sun setting in the evening creates additional loads that must be addressed. This is not
a concern for southeastern exposures, however, because the radiation from the rising sun is mitigated by
coolth collected overnight.

Figure 48: Sun path diagram showing sun position and outdoor temperature
Both BAU and ER models follow this pattern, however the effects of orientation are more pronounced for
the BAU cases. The range of cooling values for BAU30 is 8.4 kBtu/ ft
2
/yr while the range for ER30 is 2.37
kBtu/ ft
2
/yr. This finding is surprising because both models experience the same solar gains (which are the
primary driver for increasing cooling loads). The difference may be the result of the ER model’s use of
natural ventilation and wind, which is also affected by orientation. According to Climate Consultant, the
dominant wind direction in Los Angeles is northerly (from the south). This can help explain why the southern
orientations experience the greatest potential energy reductions.
83

Figure 49: Wind rose for downtown Los Angeles (Climate Consultant 5.5)
5.3.1.2 Effects of WWR
The effects of orientation are more pronounced based on the zone’s WWR. The amount of glazing is the
key factor in how much direct solar radiation enters the space. The more glazing there is (higher WWR
values) the more radiation can enter the space, and more energy is required to remove it. Even orientations
that do not get direct sun (north, for example) experience higher cooling loads with higher WWRs.
Both BAU and ER models experience the same increase in cooling loads with increased WWR, and as
discussed above, the range of cooling load values increases for the BAU model compared to the ER model
as WWR rises. The range of values for both BAU and ER models increase with higher WWR values as
well. The range of values for BAU20 is 5.06 kBtu/ ft
2
/yr while the range for BAU50 is three times greater at
15.31 kBtu/ft
2
/yr. ER models show the same pattern, with the range for ER20 at 0.99 kBtu/ft
2
/yr compared
to 7.17 kBtu/ft
2
/yr for ER50.
Wind could also be playing a part in the effects of WWR on cooling loads for ER models. Natural ventilation
calculations are done using the operable window area for each zone. A higher operable window area lets
in more air which cools the space, which could explain the smaller difference in range values. However,
the more significant factor appears to be wind direction.
5.3.2 Photosensor-Controlled Lighting Systems
Lighting is a major component of electricity use in office buildings. Energy consumption for lighting is high,
but unnecessarily so, as most of an office’s occupied hours coincide with daylight hours. Lighting schedules
and systems are usually configured as “always on”, meaning that lighting systems are using energy
regardless of light levels in the space. Using photosensor-controlled lighting systems is one way to limit
electric light usage, by only activating systems when natural light is insufficient. The ER model simulated
this type of lighting control by using the inverse of the daylight autonomy with the same lighting power
density. Because the lighting loads were determined based on lighting power density and the zone area,
BAU lighting loads do not change based on orientation or WWR. Energy reductions for all simulations of
the ER model averaged to 45% with the highest reduction of 61.21% (8.11 kBtu/ft
2
/yr) and the lowest
reduction of 25.81% (3.42 kBtu/ft
2
/yr). The percent savings is significant, however in terms of total energy
84

consumption, these reductions are less than those derived from mixed-mode ventilation. In addition, real-
world savings would be less, because of inefficiencies in lighting systems and controls, occupant override,
and architectural features such as internal partitions which can block daylight.
5.3.2.1 Effects of Orientation
The effects of orientation on lighting are opposite to the effects of orientation on mixed-mode system
integration and cooling loads discussed above. As a zone’s orientation is directed to the south, its lighting
load decreases if that zone utilizes photosensors with lighting controls. This makes sense for the same
reasons that make cooling loads rise for southern exposures: the sun in the southern sky casts more direct
light onto these orientations, letting more light enter the space, reducing the need for electric lighting.
Reductions caused by changing the orientation are fairly consistent, even across WWR. The maximum
range of lighting loads occurs in ER40 at 1.69 kBtu/ ft
2
/yr. The smallest range differs by only 0.28 kBtu/
ft
2
/yr. This shows that though orientation does have an effect on the lighting load, it is not the driving force
behind the reduction illustrated through the simulations.
5.3.2.2 Effects of WWR
There is a clear relationship between WWR and lighting loads. The data show that increasing a zone’s
glazing increases the daylight available, thus lowering the required lighting energy. The ranges for changes
in WWR (grouped by orientation) are about twice as large as the ranges for changes in orientation. The
largest ranges (3.21 kBtu/ ft
2
/yr) occur for the northeasterly orientations. As with effects due to orientation,
the changes that are attributed to WWR are fairly constant (the difference between the maximum range
and the minimum range is only 0.24 kBtu/ ft
2
/yr). The fact that more change (an average of 3.13 kBtu/ ft
2
/yr
compared to 1.56 kBtu/ ft
2
/yr) is attributable to WWR rather than orientation shows that WWR is the primary
driver for savings in lighting load when photo sensors are used for lighting controls.
5.4 Discussion of Limitations
This research is far from exhaustive, and only begins to explore the potential for energy reduction in Los
Angeles’s historic building stock. The following sections discuss the limitations of the methods used for
these simulations, as well as some possible solutions to overcome these hurdles.
5.4.1 Idealized Circumstances
One of the biggest issues for these simulations (and for most simulations for that matter) is that the
situations, systems, and occupants are simulated as ideal. That means that assumptions about the best
possible uses of energy and ways to harness free cooling and lighting are used to approximate energy
reductions.
An example of this is how lighting load reduction was calculated in this research. Daylight autonomy, a
figure which represents how well a space is daylit over a given threshold during specified hours as a
percent, was calculated for each zone. The remaining time (100% minus DA%) required electric lighting.
The percent of time requiring electric lighting was multiplied by the total lighting load in the BAU case, which
used electric lighting at all times.
This calculation method makes a few key assumptions. First, it assumes that all of the available natural
light is accounted for by the photo-sensing devices. Second, it assumes that electric light is only used in
areas where daylight is insufficient. Third, it assumes that occupants will not turn lights on, despite having
sufficient daylight. In real-world situations, this is rarely the case. Inefficient sensors and occupant override
often result in greater lighting loads than the idealized simulation, and prevent greater levels of market
penetration for photosensor devices (Bierman, Pysar, and Figueiro 2003).
85

The natural ventilation calculations are also based on assumptions that do not necessarily reflect real-world
conditions. For example, the calculation for wind driven ventilation is likely based on the assumption that
the zone has an ideal air outlet. Ultimately, what this means is that the simulation is run as if the zone was
double-loaded (had windows on two opposite sides, more commonly called cross ventilation) rather than a
single window. Though many of the buildings were designed with floorplates specifically to allow cross
ventilation, it cannot be assumed that modern tenants have maintained that option, as the installation of
internal partitions is very common, and their presence greatly reduces cross ventilation. It should also be
considered that the simulations do not model zones that have obstructed access to wind, be it by
neighboring buildings or other wings of the same building (as with the STB and other alphabet buildings).
This obstruction effects how much air can enter the zone, and therefore how much free cooling the zone
can harness.
The simulation also does not match real-world conditions in terms of final cooling and heating loads. The
simulations for both BAU and ER models were conducted using ideal air loads for determining cooling and
heating loads. These loads represent the amount of heat that needs to be added or removed from the
space, not the energy used to condition the space. This method was chosen because a wide variety of
systems could be chosen by retrofit teams to meet a zone’s thermal loads. For example, the heating needs
of a historic building could be (and often are) met with a gas-burning furnace, yet the differences between
electric and gas heaters and the wide range of efficiencies available is not reflected in the Honeybee
outputs. The same is true of cooling loads, which could be halved, or even quartered, when the efficiency
of a heat pump is included in cooling load calculations (Murphy 2012).
5.4.2 Shading Devices
A key passive strategy not included in these simulations was the use of exterior shading devices. Devices
such as overhangs and fins have the potential to further reduce cooling loads (the largest loads on the
zone) though at the expense of daylight autonomy. The addition of these kind of shading devices may be
appropriate for some of Los Angeles’s older buildings, however extreme care should be exercised,
especially with buildings that are registered historic-cultural monuments (HCM). Preservation of a building’s
distinctive façade features may take precedence over the installation of new shading devices, particularly
if the building did not use similar devices previously. Further simulation is required to determine the city-
wide effects of shading devices. The appropriateness of these devices must be determined on a case-by-
case basis, but for the sake of more conservative results, they were not considered in these calculations.
Interior shading devices and modified shading schedules were also not included, however future
simulations could include them, as they are much more appropriate for a wider range of historic buildings.
Interior shading strategies typically pose less risk to historic fabric for installation and do not typically alter
the overall character of a building in any significant way. When modeled properly (with appropriate
schedules) these savings strategies could also yield additional savings.
5.4.3 Thermal Mass
Another passive strategy which was not specifically modelled was the building's thermal mass. Historic
construction of large commercial buildings in downtown Los Angeles typically utilized materials with a high
thermal mass and low thermal inertia, meaning that the mass of the building could be used to absorb heat
during hot days and release it during cool nights. When the mass of the building is coupled with the space
(meaning that insulation is installed on the outside rather than on the inside), considerable energy savings
are achievable. When used in conjunction with natural ventilation, energy savings ranging from 17% to
43% can be expected over buildings with non-coupled thermal mass (Stazi et al. 2014). For conventionally
conditioned spaces with coupled thermal mass, energy savings of about 12% are expected (Ma and Li
2010).
86

5.5 Deliverables
The main objective of this thesis was to determine whether or not the reintegration of these strategies alone
would allow Los Angeles to meet its climate goals for existing historic buildings. While this does not appear
to be the case, the results still indicate substantial energy savings and the research process has yielded
some tools that can be useful for city officials seeking to guide decision-making for the future of Los Angeles
as well as owners of historic buildings attempting to perform energy retrofits.
5.5.1 Calculation Methodology
The methodology developed for these simulations can be used to determine a first order estimate of
potential energy reduction for any historic building that shares similar construction techniques and
materials. The lookup tables that were produced with the results of the simulations can be used for quick
decision-making when prioritizing possible actions, and indicate where more analysis is needed to
maximize savings (which would be determined on a case-by-case basis).
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6 Conclusion
From the outset of this research, the intended goal was to determine whether or not the reintegration of
passive strategies (which had previously been negated by retrofits introducing conventional HVAC
systems) to historic pre-war commercial buildings in downtown Los Angeles was sufficient for meeting
state-mandated carbon emissions reductions goals. Meeting these goals requires significant changes to
the existing building stock across the state that would generate energy savings of 40%. To determine if
this reduction was possible for Los Angeles’s buildings utilizing the strategies that were already inherent in
their design, paired simulations were run to find relative energy reductions. Two models, one representing
the sealed historic building with conventional HVAC and lighting systems and the other representing the
building with environmentally responsive cooling and lighting systems, were created and tested for a series
of orientation and window-to-wall ratios. Though based on a real building, the model was sufficiently
generic enough to be applicable to much of the building stock, which shares many features of construction,
material, size, and shape.
6.1 Los Angeles Climate Goals
As discussed previously, for carbon emissions levels in California to be 80% below 1990 levels by 2050,
existing buildings must be retrofit to reduce energy consumption by 40% (Long 2011). The simulation data
show that while there is a significant energy reduction as a result of the reintegration of passive lighting and
cooling strategies (through photosensor-controlled lighting and mixed-mode ventilation). Energy
consumption is expected to drop 28.92% across the pre-war building stock, which falls short of the goal to
be compliant with state requirements. However, this is not a failure. First, the data is valuable. It is
important to know that more needs to be done to meet the goals that have been set. Knowing this
information for a city-scale can assist city planners and officials in making policy decisions to require and/or
promote certain retrofit strategies.
6.2 Environmentally Responsive Design
Much of the data gathered through this research validated intuitive feelings about the passive design
strategies analyzed. That passive daylight strategies would reduce the lighting load makes sense, and of
course that reduction is directly related to the windows size and orientation. The case is similar for mixed-
mode ventilation, which decreases the use of mechanical systems therefore reducing the loads as would
be expected.
Not all of the simulations yielded results that were expected, however. For example, the thermal autonomy
of an unsealed, unconditioned zone was measured to hover around 30%. This value seems low when
compared to the relatively mild climate of downtown Los Angeles. This should be checked against other
simulations of naturally ventilated spaces to confirm the accuracy of the thermal autonomy figures.
6.3 Future Work
The work conducted for this research is only the beginning. More advanced models should be developed
to continue to give the city the tools it needs to make intelligent decisions regarding the treatment of historic
buildings when analyzing energy retrofit options.
6.3.1 Integration with GIS Data
First order estimates must be used when quantifying building-stock level results largely because the
information that is necessary to make more exact calculations is not readily available. However, additional
work could draw from existing GIS and County Assessor resources to produce energy consumption data
on a building-by-building level. GIS stands for geographic information system, and is a way of organizing
88

and manipulating geographical and spatial data. GIS has a wide range of applications from mapping
customer locations to seeing where water would flow in a flash flood event. Its versatility has made it an
invaluable tool for city planners, who need to organize information about resources, demographics, and
buildings into usable formats. A resource recently made available to the public is the countywide collection
of building outline shapefiles: a collection of over 3,000,000 building footprints with height, area, and parcel
number data gathered as art of the LAR-IAC2 Project in 2008. This vector data offers users precise building
orientation and context, and is essential for further developing this research.

Figure 50: Sample of building outline data generated as part of the LAR-IAC2
Project (Los Angeles County CIO Office 2012)
The building data is useful and manageable when looking at a small number of buildings at a time, but the
task of using all of the orientation data in the shapefile to generate individual building energy estimates is
enormous and impractical. Automating the process is crucial. A script capable of analyzing the shapefiles
to determine façade length and orientation should be developed to automatically link back to the reduction
factors, allowing for more accurate city-wide estimations. To truly automate the process, a script capable
of calculating WWR for each of the buildings, perhaps based on street-level imagery, would also be
necessary.
6.3.2 Shading and Thermal Mass
The results presented in this thesis should be considered the most conservative energy reduction
estimates. As discussed in the previous chapter, other simple and common environmentally responsive
passive design strategies were not included in these analyses, but should be considered for any future
research. These include both internal and external shading devices and the effects of coupled thermal
mass.
Shading requires the modeling of actual shading devices and schedules to control them, if they are
dynamic. Getting these schedules to match real-world use can be difficult, however close approximations
should be sufficient. The real key will be first determining appropriate forms of shading that respect a
building’s historic character, and second, finding the most efficient configuration of that shading device, with
the optimal tradeoff of daylighting and heat gain. Numerous studies have already explored this tradeoff, so
its application to historic buildings is not a large leap.
Thermal mass is an inherent property of a material, though special considerations need to be made when
modeling it for energy analysis. The material must be properly placed in relation to the indoor environment,
89

and strategies such as night-flush ventilation should be explored with it, to make full use of the material’s
time lag and decrement factors.
6.3.3 Integration with Required Seismic Retrofits
The City of Los Angeles is spending considerable effort to identify buildings that are at risk in the face of a
seismic event. Because the risk to life and property is high, retrofits have been mandated by the City for a
building to remain habitable. Energy retrofits, which do not have an immediate effect on the life-safety of a
building are not viewed as equally necessary for the moment, but adoption of these retrofits could be
increased if they can be effectively implemented while the mandatory seismic alterations are made. Future
research should focus on the construction and retrofit methods used while performing a seismic upgrade
and find synergies and opportunities for the most effective simultaneous integration of energy retrofits.
6.3.4 Analysis of Financial Return
The push for owners to perform historically sensitive energy retrofits will need to come from a variety of
fronts if all existing buildings are to be retrofit to reduce consumption by 40%. Certain performance
thresholds will likely need to be mandated by city officials to drive most of the energy consumption down,
but incentives will also play a vital role. An in-depth analysis of the financial costs of performing the energy
retrofit compared to the savings that result will be needed to help convince building owners that these
retrofits are worthwhile. Analysis should include the return on investment, financial savings of purchasing
less energy electricity and fuel, any benefits from increased employee productivity and happiness, and the
effects of these improvements on rent per square foot.
6.4 Final Remarks
“Undo” is not always an option when it comes to buildings, especially those that are historic in nature. Some
things once lost, are lost forever, but luckily this is not the case for the passive strategies that were an
inherent part of many historic buildings’ design. It is inevitable that some aspects of a building will change
over a long life; sustainability demands flexibility in this regard. But these changes must be carefully
considered. The alterations to historic buildings that sealed envelopes and added energy intensive
mechanical systems undoubtedly improved interior comfort conditions, or at least made them more
controllable. But they were, for the most part, carried out at a time when energy was cheap and the dangers
of climate change were not as imminent as they are today. Sensitively undoing these changes not only
restores pieces of historic fabric and character, but it also makes the building more able to withstand the
climatic demands of the changing environment. Though the strategies analyzed in this research may not
be enough to meet Los Angeles’s climate goals alone, they are the first step to embracing the past and
preparing for the future.
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Abstract (if available)

Abstract To achieve the 80% statewide greenhouse gas reduction target mandated by California Executive Order S-3-05, the existing building stock must become 40% more energy efficient by 2030. Meeting the target for existing buildings will require deep energy retrofits to the 465-million square meters of existing commercial building stock in the next 15 years. While studies have shown that energy retrofits leading to reductions as high as 28% are possible in a cost-effective manner, and that “deep” energy retrofits can achieve even greater reduction, these recommendations are often poorly applicable to historic buildings for two reasons. First, the recommendations nearly always promote incremental improvements to a sealed building envelope and efficiency upgrades to mechanical HVAC and lighting systems. This approach overlooks the possibility of meeting energy reduction targets simply by re-activating the passive strategies present in many historic structures, which have largely been de-activated over time following past renovations which introducing air conditioning and fluorescent lighting technologies. Second, many retrofit recommendations would alter the character of historic structures to an extent that would be unacceptable to preservationists, the public, and existing historic preservation laws. ❧ Because a large portion of the existing commercial building stock in Downtown Los Angeles was built before 1950 and originally designed to rely on the local climate for most Indoor Environmental Quality (IEQ) needs, an alternate approach to deep energy retrofits is needed which begins by examining the energy reductions achievable through re-activation of these original passive strategies. ❧ To address this need, a simulation-based framework was developed to compare annual energy and IEQ outcomes from re-activation of multiple passive strategies. Detailed building information on LA’s Subway Terminal Building (with observational analysis of ten additional pre-1950s buildings) was used as a basis to develop the initial baseline model and identify original passive design strategies (e.g. exterior solar control, daylighting, and natural ventilation). Energy and daylight simulations using the EnergyPlus and Radiance engines are used to quantify annual performance outcomes from a parametric exploration of retrofit combinations that replicate and improve upon the original passive design intent of the historic building type. Compared to the baseline model, implementation of the best set of passive retrofits was found to yield a reduction in Energy Use Intensity (EUI) of 29%. Due to the typical design conventions of this building type, a parametric model was developed to extrapolate the potential of re-activating passive strategies for additional historic buildings. Analysis of the metric thermal autonomy revealed that thermal comfort conditions can be met in pre-1950s buildings for large periods (up to 67%) of occupied hours utilizing only passive conditioning. Analysis of the metric daylight autonomy revealed that floor plates could be effectively daylit for up to 61% of occupied hours, depending on solar orientation. The paper concludes with discussion of implications and limitations of the simulation framework for urban scale evaluation of passive performance in pre-1950s buildings.

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Asset Metadata

Creator Becker, Geoffrey S. (author)

Core Title ctrl+z: exploring the effects of undoing retrofits to pre-war buildings in Los Angeles

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Publication Date 07/28/2015

Defense Date 05/01/2015

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

Tag building stock,daylight autonomy,EnergyPlus,historic building,OAI-PMH Harvest,passive design,Radiance,simulation,thermal autonomy

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Konis, Kyle (committee chair), Carlson, Anders (committee member), Lesak, John (committee member), Noble, Douglas (committee member)

Creator Email gbecker@usc.edu,itsgeof@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-610190

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Legacy Identifier etd-BeckerGeof-3724.pdf

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