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Analyzing the Impact of Outside Temperatu re on Energy Consu mption and
Production Patterns in High-Performance Research Buildings in Arizona
Article    in  Journal of Ar chit ectural Engineering · Februar y 2017
DOI: 10.1061/(ASCE)AE.1943-5568.0000242
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Analyzing the Impact of Outside Temperature on Energy
Consumption and Production Patterns in High-Performance
Research Buildings in Arizona
Fernanda Cruz Rios1; Hariharan Naganathan2; Wai K. Chong, M.ASCE3; Seungtaek Lee4;
and Anderson Alves5
Abstract: The intimate relationship between energy consumption and climate change demands attention. More energy will be needed to run
cooling systems if the annual global temperature continues to rise. The urban heat island would also increase the demand for cooling. As globalenergy demand continues to grow, the utility sector would face a continuous increase in energy demand. Studies in several countries haveshown mostly nonlinear relationships between outside ambient temperature and electricity consumption, whereas other studies have suggestedthe absence of such relationships among high-performance buildings. However, these studies were based on aggregate data from entire cities
and/or countries (indirect relationships) and were not based on real-time data (direct relationships). This study uses continuous real-time data
from four high-performance research buildings and presents the results from a set of correlations and regression analyses between several vari-ables, i.e., outside temperature, heat index, electricity consumption, and the production of solar energy. The authors found no relationshipbetween electricity use and outdoor temperature, and between electricity use and heat index. Conversely, the ef ficiency of the production of so-
lar energy was affected negatively by higher outdoor temperatures. DOI: 10.1061/(ASCE)AE.1943-5568.0000242 .©2017 American
Society of Civil Engineers.
Introduction
The average annual temperatures across the United States have
increased approximately by 0.9°C over the last 100 years ( U.S.
DOE 2013 ). The energy sector is intimately tied with global climate
change; thus, it generates signi ficant impact on our economy and
environment ( McCarthy 2001 ;Wang and Chen 2014 ). Studies indi-
cated that building energy consumption is intimately tied to changesin outdoor temperature, and such relationships had become increas-
ingly signi ficant over the last decades ( Bessec and Fouquau 2008 ;
Lee and Chiu 2011 ). Several studies have assessed the in fluence of
climate change and increasing temperatures on the demand for
energy in different countries ( Henley and Peirson 1997 ;Bessec and
Fouquau 2008 ;Bigano et al. 2006 ;Fan et al. 2015 ;Lee and Chiu
2011 ;Moral-Carcedo and Vic /C19ens-Otero 2005 ). Table 1presents a
summary of these studies.
In the United States different types of commercial buildings
accounted for 73% of all electricity generated and consumed inthe country, and over half of it was used for the heating and cool-
ing of spaces ( U.S. Green Building Council 2015 ;U.S. DOE
2011 ). Thus, it is critical to understand energy consumed by heat-
ing, ventilating, and air-conditioning (HVAC) systems and the
factors that in fluence HVAC performance. Energy use patterns
and their relationships with ambient temperature have been inves-
tigated by many researchers in different climatic zones ( Wang
and Chen 2014 ;Hart and de Dear 2004 ;Coskun et al. 2014 ;Zhou
et al. 2014 ;Lin and Claridge 2015 ;Frank 2005 ;Wan et al. 2012 ;
Olonscheck et al. 2011 ;Kolokotroni et al. 2012 ;Dodoo et al.
2014 ;Berger et al. 2014 ;Kalvelage et al. 2014 ). However, most
of these studies used aggregate data (rather than sectorial data)
and prediction models for an entire region. For example, Moral-Carcedo and Vic /C19ens-Otero ( 2005 ) investigated the response of
electricity demand to temperature variations using aggregate data
from Spain. Other studies have analyzed the energy consumptionat the building level, using mostly models, modeled buildings,
and/or base on simulated scenarios. Wang and Chen ( 2014 ), for
instance, simulated two types of residential buildings and seven
types of commercial buildings in 15 cities in the United States.
Fewer, if any, studies have been based on real-time continuousdata.
Studies have also found that outside temperature in
fluenced
energy consumption patterns and how it is used to model energysupply ( Sailor 2001 ). Some researchers have developed models
to predict the consequences of increasing ambient temperatures
on energy consumption ( Coskun et al. 2014 ;Santamouris et al.
2014 ;Wang and Chen 2014 ;Bessec and Fouquau 2008 ;Zhou
et al. 2014 ;Kalvelage et al. 2014 ). They have concluded that the
consequences varied across different geographic locations. Zhou
et al. ( 2014 ) performed a study to evaluate the future energy
demand in the United States, and their results indicated thatArizona will be among the states with the largest increase in elec-
tricity demand in the next 50 years. In a state with temperatures
that frequently exceed 43°C during the summer, constant1Ph.D. Student, School of Sustainable Engineering and the Built
Environment, Arizona State Univ., Tempe, AZ 85281.
2Ph.D. Student, School of Sustainable Engineering and the Built
Environment, Arizona State Univ., Tempe, AZ 85281.
3Associate Professor, School of Sustainable Engineering and the Built
Environment, Arizona State Univ., Tempe, AZ 85281 (correspondingauthor). E-mail: ochong@asu.edu
4Ph.D. Student, School of Sustainable Engineering and the Built
Environment, Arizona State Univ., Tempe, AZ 85281.
5Undergraduate Student, School of Sustainable Engineering and the
Built Environment, Arizona State Univ., Tempe, AZ 85281; United States/
Federal University of Piauí (UFPI), Teresina 64049-550, Piauí, Brazil.
Note. This manuscript was submitted on October 12, 2015; approved
on November 7, 2016; published online on February 24, 2017. Discussionperiod open until July 24, 2017; separate discussions must be submitted
for individual papers. This paper is part of the Journal of Architectural
Engineering , © ASCE, ISSN 1076-0431.
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Table 1. Contributions from the Previous Literature about Understanding the Relationship between the Ambient Temperature and the Electricity
Consumption in Buildings
Country/region Reference Data characteristics Findings
U.S. ( Sathaye et al. 2013 ) Regional, aggregate data (California) Estimated impacts were based on prediction models for
future scenarios; higher temperatures increased peak elec-tricity demand
(Kalvelage et al.
2014 )Predicted weather data in multiple climate
model scenarios; used typical building con-struction for five U.S. citiesResults suggested that current buildings in the United
States will have their future heating demand reduced, andtheir cooling demand increased; the authors addressed the
humidity factor as part of the electricity demand equation,
markedly for cooling purposes; results varied among thecities; they stated that passive design strategies can miti-gate the rise in electricity consumption
(Zhou et al. 2014 ) Regional, projected temperature data; com-
pared regional with national data; performedprojections based on population and gross
domestic product data; developed a database
from energy models; studied both residentialand commercial sectorsIn all states, heating demand decreased, whereas cooling
demand increased; the net effect varied across regions; fac-tors such as policy, population growth, and gross domestic
product had a relevant impact on buildings ’energy usage;
this impact varied across the states; the authors suggestedthat the general model in this research should be improved
with better assumptions, particularly concerning construc-
tion technology and variations in building envelopes fromstate to state
(Wang and Chen
2014 )Modeled future weather data for 2020, 2050,
and 2080 for 15 U.S. cities; analyzed threeCO
2emission scenarios; the simulation
addressed two types of residential buildings
and seven types of commercial buildingsConfirmed that the impact varied depending on buildings ’
geographical locations; the results were provided asnationwide averages; depending on the different climatezones, the net energy consumption was estimated to
increase or decrease; con firmed that the impact of climate
change varied signi ficantly among different types of
buildings
Canada ( Mohareb et al.
2011 )Aggregate, real high-performance building
data; studied energy use only for heating pur-poses, from various sources (fuel-based and
electricity)Found no relationship between HDDs and EUIs for a sam-
ple of 57 green buildings in different countries; stated thatenergy-ef ficient technologies and design strategies have
been successful in isolating the indoor heat losses of out-
side air temperature; provided empirical evidence that EUIand climate do not correlate for their sample of green
buildings
24 OECDCountries(Lee and Chiu 2011 ) Aggregated data; applied a nonlinear model to
relate electricity consumption and tempera-ture (1984 –2004)Confirmed a U-shaped relationship between electricity
consumption and temperature; determined an averagethreshold value of temperature for this relationship; con-
firmed that the impact of temperature on energy consump-
tion has increased in recent years
U.K. ( Kolokotroni et al.
2012 )Predicted weather data in a simulated build-
ing; electricity demand data were assumed in
the modelPredicted the future impacts of the urban heat island effect
on the electricity consumption of a modeled of fice building
in London; heating loads decreased, cooling loadsincreased, and CO
2emissions were expected to rise; the
increase in the cooling loads varied according to the type
of construction and internal heat gains
(Henley and Peirson
1997 )Aggregated data on household demand for
electricity (1-year period)Found a nonlinear relationship between the temperature of
the outside air and the use of electricity by the residential
sector in the United Kingdom
Spain ( Moral-Carcedo and
P/C19erez-García 2015 )Real, sectorial data (2009 –2013) from firms Aggregate electricity demand in Spain ’sfirms was insensi-
tive to temperature; sensitiveness varied across sectors;
service sector firms had the highest sensitiveness
(Moral-Carcedo and
Vic/C19ens-Otero 2005 )Aggregated data from different regions of
SpainExplored the nonlinear, U-shaped correlation between
temperature and energy consumption; studied the valida-
tion of threshold temperatures for HDDs and CDDs
EuropeanUnion(Bessec and
Fouquau 2008 )Aggregated data for 15 European countries
over two decadesConfirmed the nonlinearity between temperature and elec-
tricity consumption; found that the nonlinear pattern was
more noticeable in hot countries; results also showed an
increase of the sensitivity of electricity use to outsidetemperatures
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exposure to heat will lead to thermal discomfort, and energy con-
sumption will increase rapidly to meet cooling needs. Energy pro-
duction must increase during the summer months to meet the
increased energy demand. Dramatic decreases in temperatureduring the winter require increased energy production, either by
generating electricity or providing natural gas. Increasing energy
production led to increased carbon emissions ( Kibert 2013 ).
Carbon dioxide emissions were associated with the increase in
energy demand during cooling days ( Berger et al. 2014 ).
The use of envelope materials with elevated thermal resistance
i sk n o w nt ob ee f f e c t i v ei nr e d u c i n gh e a tt r a n s f e rf r o mt h eo u t s i d e
air to the indoor environment. Therefore, the larger the thermal
resistance of the envelope materials is, the more isolated theindoor temperature is from the outside temperature. A recent
study found that electricity consumption was not correlated with
outdoor temperature among the studied high-performance build-ings ( Mohareb et al. 2011 ). An energy-ef ficient envelope can
overcome the effects of the outside temperature on indoor heating
and cooling loads by reducing the transfer of energy through wall
(Fang et al. 2014 ;Wong et al. 2013 ). Energy waste would be
reduced if there is a better understanding of how different factorsaffect energy consumption in real time.
The aim of this research is to develop an understanding of the
relationships between outside temperature, heat index, electric-ity use, and energy production in high-perform ance research
buildings. The sample building t hat was selected for the study
had recognized high-quality design and energy-ef ficiencyfeatures. The authors coll ected data from Arizona State
University (ASU).
Background
The commonly acknowledged factors that in fluence building
energy consumption include weather (e.g., outside temperature, hu-
midity, wind); the building ’s characteristics (e.g., orientation, enve-
lope materials, windows); occupancy and occupants ’behaviors
(e.g., consumption patterns, thermal comfort perceptions); types of
energy-consuming fixtures and systems (e.g., HVAC system, eleva-
tors, numbers and types of computers, refrigerators, and other appli-
ances and devices); building ’s size, typology, and orientation (e.g.,
a large commercial building that has a large number of users andappliances that generate heat versus a single-family house in which
heating and cooling loads are the main consumers of energy); and
local energy policies and utility pricing (e.g., building and utility
codes, regulations, standards, incentives).
This paper focuses on the effects of outside temperature and hu-
midity on building energy consumption. Outside temperature and
humidity are in fluenced by wind speed and external environment.
The wind speed in Arizona varies from 6 to 8 knots (light to gentle
breeze). These values correspond to 2 and 3 on the Beaufort wind-
speed scale ( Wind finder 2015 ), which corresponds to low convec-
tive heat transfer. In addition, few buildings at ASU have operableTable 1. Continued
Country/region Reference Data characteristics Findings
Austria
(Vienna)(Berger et al. 2014 ) Modeled regional climate data applied to
simulated thermal data from nine existingoffice buildingsHeating demands were reduced slightly, whereas cooling
demands increased signi ficantly; the impact varied across
buildings from different periods of construction
China ( Yu et al. 2012 ) Forecast weather data for 2020, 2050, and
2080; Data were based on a typical, regional
weather year (Hong Kong); hourly HVACdata were collected from the energy model for
a simulated of fice buildingInvestigated the impact of climate change and future
warmer temperatures on the design and operation of chiller
systems; the findings indicated that the systems should
increase their capacity to meet the increasing cooling
demand over their life spans; the electricity consumption
of the equipment was also expected to increase because ofhigher outside temperatures
(Fan et al. 2015 ) Aggregate, sectorial, monthly based data for
energy consumption; the data were separatedbetween northern and southern China; CDDs
and HDDs were used as temperature dataThey found that the infrastructures of their residential
buildings and tertiary industries were more sensitive totemperature variation than primary and secondary industry
infrastructures
Singapore ( Wong et al. 2013 ) Sectorial data (residential) from simulated
buildings; projected weather dataStudied mitigation methods for the impact of increasing
outside temperatures on indoor heat gain and coolingdemand; found that an ef ficiently designed envelope alone
could bring the predicted larger cooling demand in the
future back to the present level
Thailand
(Bangkok)(Wangpattarapong
et al. 2008 )Real, monthly based data of climate factors,
including temperature, humidity, wind, and
rainfall, over 20 years; used residential energyconsumption dataForecasted residential electricity consumption; analyzed
the factors that in fluenced the electricity consumption; the
results showed that the average ambient temperatures hada signi ficant impact on the consumption of electricity
Kuwait ( Ayyash et al. 1985 ) Aggregated, monthly based data Models suggested that an increase of CDDs meant higher
monthly consumption
Bahrain ( Radhi and Sharples
2013 )Aggregated, hourly based data The analysis pointed to an increase of CDDs in metropoli-
tan centers as a consequence of the urban heat island
effect; as a result, the domestic demand for electricity
tended to increase; the increase varied with land use (sec-tors) and the thermal properties of the urban surfaces
Note: CDDs = cooling degree days; EUI = energy use intensity; HDDS = heating degree days; HVAC = heating, ventilating, and air-conditioning systems;
OECD = Organization for Economic Cooperation and Development.
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windows; thus, this research does not consider the effects of natural
ventilation and wind effect on energy consumption.
Several publications established the relationship between out-
door temperatures and peak energy demand ( Santamouris et al.
2014 ;Wang and Chen 2014 ). Santamouris et al. ( 2014 ) concluded
that peak demand energy increased between 0.45 and 4.60% perdegree of increase in the outdoor ambient temperature, dependingon the geographical region. In turn, the total demand varied from0.5 to 8.5% per degree of increase in the ambient temperature, andthis percentage was 3.6% in Phoenix ( Santamouris et al. 2014 ). A
summary of the contributions from past studies is shown in Table 1.
Climate Change and the Consumption of Energy
by Buildings
Some authors developed prediction models to assess how higher
temperatures in the future would impact energy consumption andmarkedly affect energy loads for heating and cooling ( Wan et al.
2012 ;Xu 2009 ;Frank 2005 ;Dodoo et al. 2014 ;Berger et al. 2014 ;
Yu et al. 2012 ;He et al. 2009 ;Wang and Chen 2014 ;Kolokotroni
et al. 2012 ;Kalvelage et al. 2014 ). Several studies produced similar
outcomes, exhibiting the relationships between higher outside air
temperature and energy consumed for cooling, whereas heating sys-tems are expected to consume more energy when temperaturedecreases ( Wang and Chen 2014 ;Frank 2005 ;Wan et al. 2012 ;
Olonscheck et al. 2011 ;Kolokotroni et al. 2012 ;Zhou et al. 2014 ;
Dodoo et al. 2014 ;Berger et al. 2014 ;Kalvelage et al. 2014 ). Zhou
et al. ( 2014 ) stated that heating systems usually require more energy
than cooling systems because the heating technology is lessadvanced ( Zhou et al. 2014 ). Cooling systems, on the other hand,
are more sensitive to outside temperatures than heating systems(Hart and de Dear 2004 ;Frank 2005 ;Asimakopoulos et al. 2012 ).
Building energy consumption is in fluenced by many factors beyond
outdoor air temperature, for example, the Yahhoobian and Kleissl(2012 ) energy estimation model for estimating cooling/heating
loads and energy use in buildings includes buildings ’envelopes and
shapes and the properties of their materials, surrounding environ-
ment, weather conditions, urban microclimate, indoor heat sources,
infiltration, and HVAC equipment ( Yahhoobian and Kleissl 2012 ).
This research focuses on controlling the other factors ’effects in
affecting energy consumption and isolating outdoor air temperatureas the dominate factor.
Outdoor Temperature Impacts on the Energy
Consumption of Buildings
As discussed previously, studies have shown a nonlinear relation-
ship between outside temperature and the national demand for elec-tricity. Many studies found a U-shaped correlation in the EuropeanUnion ( Moral-Carcedo and Vic
/C19ens-Otero 2005 ;Henley and Peirson
1997 ;Bessec and Fouquau 2008 ;Santamouris et al. 2014 ) and
proved that extreme cold or hot weather had a direct effect on build-ing energy consumption (Fig. 1). The U shape re flects the buildings ’
performances relative to indoor comfort and the HVAC systems,and increases in the temperature of the ambient air resulted inincreased energy consumption to maintain the desired indoor tem-peratures in buildings.
Despite the symmetry illustrated in Fig. 1, the nonlinear relation-
ship between outside temperature and energy consumption in build-ings is rarely symmetric. The curve ’s actual shape depends on many
other variables, such as the regional climate, ef ficiency of energy
sources, and the performance of the buildings ’systems. Bessec and
Fouquau ( 2008 ) illustrated how these graphs would be affected by
different climates and how different fuel sources generate differentcurve shapes. Other in fluential variables include air penetration and
leakages, thermal heat transfer through an envelope, indoor air qual-
ity and comfort requirements, and speci fic electrical load require-
ments ( Santamouris et al. 2014 ). These factors are region speci fic.
However, the U-shaped relationship between temperature andenergy demand found by prior research was based on national data;thus, it did not consider regional variations. The question thisresearch plans to answer is whether such a relationship exists at thebuilding level.
Impact of the Quality of the Data on the Study
Variability, reliability, and availability of data affect the outcomes
of these studies; for example, most of the studies were performedon hypothetical buildings ( Wan et al. 2012 ;Xu 2009 ;Yu et al.
2012 ;He et al. 2009 ;Wang and Chen 2014 ;Kalvelage et al. 2014 ;
Kolokotroni et al. 2012 ). Even when existing buildings were used,
the studies focused on predicting the future behavior from their
samples ( Frank 2005 ;Dodoo et al. 2014 ;Berger et al. 2014 ) rather
than re flecting on their lifecycle and performances. The studies did
not include real-time data, such as weather. A study performed inTurkey highlighted the importance of correctly determining thedegree-hours with the smallest error when analyzing the performan-ces of heating and cooling systems ( Coskun et al. 2014 ). The
authors also stated that it was virtually impossible to get access to
data related to the distribution of outdoor temperatures for their
research. Hence, they had to develop a method to infer the coolingor heating degree-hours with the smallest amount of error.Fortunately, the U.S. weather data are readily available on an hourlybasis.
Effects of Humidity on Energy Consumption
Temperature is not the only climate variable that affects energy con-
sumption. Relative and total humidity have a signi ficant impact on
energy consumption because energy is needed to extract moisturefrom the air. Heat and moisture inside buildings come from build-ings ’occupants, equipment, and appliances ( Yang and Heinsohn
2007 ), and HVAC systems work harder to remove both heat and
moisture from the air. Although tightly sealed buildings would con-
tain conditioned air better, Seppänen and Kurnitski ( 2009 ) showed
that decreases in natural ventilation would increase in indoor mois-ture levels. Thus, buildings without operable windows are bettersamples to study the impact of weather on energy demand ( Moral-
Carcedo and Vic /C19ens-Otero 2005
). Kalvelage et al. ( 2014 ) stated that
Fig. 1. U-shaped correlation line between outside temperature ( T)a n d
electricity consumption ( E)
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managing humidity will be more important than temperature
because humidity has greater impact on the thermal comfort forpeople in buildings ( Kalvelage et al. 2014 ;Scott et al. 2007 ).
Effects of Location on Energy Consumption
The use of multiple variables in the analysis would complicate the
comparative study because the variables change with geographical
locations, especially when the variables differ signi ficantly across
the entire U.S. continent. The American Society for Heating,Refrigeration, and Air-conditioning Engineers ( ASHRAE ) Standard
90.1 divides the United States into seven thermal zones and three
humidity zones ( ASHRAE 2004 ). Each climate zone speci fies
different parameters to different variables. Zhou et al. ( 2014 )
modeled the effect of climate change in buildings in each state inthe United States and con firmed this variability ( Zhou et al.
2014 ). The modeled projections were made using the global
change assessment model, and they took into account the number
of heating and cooling days, population, and gross domestic prod-
uct variation for different states in America. The total electricitydemand was found to be lower in the heat dominance states,
because natural gas and oil are the predominant energy sourcesfor heating. Increasing temperatu re in recent winters could have
contributed to the decreasing demand for heating oil, as reported
by the congress. The net effect of outside temperature on energyconsumption varied across the regions, with the largest differencebetween cooling degree days (CDDs) and heating degree days(HDDs). Not surprisingly, the state of Arizona had one of thelargest increases in the rate at which electricity was used ( Zhou et
al. 2014 ).
Building Envelope and Energy Consumption
The Mohareb et al. ( 2011 ) study found that outside temperature had
an insigni ficant relationship to energy demand. The authors ana-
lyzed the fuel-based and electricity-based energy sources from asample of 57 certi fied green buildings from different countries and
concluded that there were little to no correlations between the num-
ber of HDDs and energy use intensity (Pearson ’sR= 0.035). The
authors attributed the relationships to the energy-ef ficient design
strategies that were used in the buildings. These strategies includetriple-paned, low-e, argon- filled windows; the low thermal trans-
mittance materials used for walls and roofs; double-skin façades;
thermal mass using passive solar; and optimized orientation forshading and heat gains ( Mohareb et al. 2011 ). The study did not an-
alyze the CDDs.
Another study compared the performance of two buildings, both
located in a hot climatic region in China, and found that the building
with a higher R-value envelope consumed 23.5% less energy for
cooling than the building with a lower R-value envelope ( Fang et al.
2014 ). A parametric study of residential buildings in Singapore
combined mitigation methods to reverse the impact of climatechange ( Wong et al. 2013 ), which showed that the researchers were
able to reduce the buildings ’cooling load by introducing a better en-
velop design. According to the research, the window-to-wall ratio,the thermal transmittance of the opaque wall, and the properties ofthe buildings ’fenestration (orientation, thermal transmittance, and
shading coef ficient) contributed signi ficantly to thermal heat gain.
A study performed in five Brazilian cities also exhibited similar
results ( Signor et al. 2001 ;Carlo and Lamberts 2008
).
Other Factors That Influence Energy Consumption
Other factors that in fluence energy consumption include buildings ’
typologies and occupancy patterns ( Ma et al. 2014 ;Goyal et al.2013 ), building types ( Fan et al. 2015 ), building functions ( Bigano
et al. 2006 ), weather, design factors, and climate. Bigano et al.
(2006 ) found that outdoor temperature affects energy consumption
differently on different building types, functions, and sectors.
Residential buildings were more susceptible to temperature varia-
tions than commercial buildings, whereas buildings in the industrialsector were independent of outdoor temperature ( Bigano et al.
2006 ). A similar study in China also found the same results ( Fan
et al. 2015 ).
The results suggested that occupants ’thermal comfort correlates
with outdoor temperatures, and controllability of HVAC systems
draws closer relationships between energy consumption and out-
door temperature. Goyal et al ( 2013 ) developed control algorithms
to assess the in fluence of the occupancy factor on buildings ’energy
savings and showed that real-time occupancy measurement and
control could lead to a signi ficant energy savings, depending on the
type of zone, weather, climate, and design factors ( Goyal et al.
2013 ).
In addition, many of the factors are correlated with one another.
For example, occupants ’ambient temperature is in fluenced by out-
door temperature and energy rate, and real-time energy demand and
energy supply is often disconnected. As a result, huge amounts of
energy often are wasted during distribution, and excess energy sup-
ply is needed to meet uncertain peak energy load. As such, better
understanding of real-time energy consumption and performance is
necessary to better align energy supply and demand. Real-time data
could be used to improve design and engineering of buildings.Engineers, designers, codes, and standards often neglect the dyna-
mism and time effects of energy consumption, thus, failing to
exploit the potential of energy ef ficiency technology and design.
Effect of Temperature on the Production of Solar Energy
The effect of operation temperature on the ef ficiency of photovol-
taic (PV) cells has been a well-established scienti fic fact. It is widely
known that heat has an adverse impact on open-circuit voltage
(Wurfel 2005 ). As the temperature of a solar module increases, the
efficiency of solar panels and power output decrease ( Wurfel 2005 ;
Skoplaki and Palyvos 2009a ;Skoplaki et al. 2008 ;Alonso Garcia
and Balenzategui 2004 ;Malik et al. 2010 ;Skoplaki and Palyvos
2009b ). The ef ficiency of PV cells depends on their operational
atmospheric temperature, which usually is described as temperaturecoefficients of current, voltage, and power ( Koehl et al. 2011
).
Temperature is in fluenced by optical properties (cells, glazing,
encapsulates, and backsheets), electrical ef ficiency of cells, and am-
bient heat transfer ( Koehl et al. 2011 ,2012 ). The temperature of the
module, in turn, depends on many internal (e.g., the module ’s mate-
rials and the respective absorption properties) and external factors
(e.g., wind speed, solar irradiance, and ambient temperature).Different types of PV cells are expected to be sensitive to operating
temperature based on the technologies they use. Research suggested
a linear and nonlinear relationship between outdoor temperature
and solar panel performance ( Skoplaki and Palyvos 2009a ;Malik
et al. 2010 ).
Koehl et al. ( 2011 ) developed a model to optimize the tempera-
ture of solar cells based on the external temperature and ambient cli-matic conditions, and established a realistic, nominal, module tem-
perature to overcome the effects of temperature on operating cells.
In addition, they also studied and proposed test procedures to exam-
ine the effects of humidity on the production ef ficiency of solar
power. Schwingshackl et al. ( 2013 ) analyzed the effect of wind on
the surface temperature and performance of the PV module using a
commonly used model to measure the temperature of a solar cell.
The model includes the temperature of the ambient air and in-plane
irradiance measurements, and it does not include wind effect
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(Skoplaki and Palyvos 2009a ). PV modules that are less sensitive to
temperature are preferable for arid and hot climates, whereas those
that are more responsive to temperature are better for the cooler
regions ( Dubey et al. 2013 ).
Z h a n ge ta l( 2014 ) developed various technologies that could be
used to predict the production of solar energy and losses due tohigher surface temperature, in which the PV-thermoelectric (TE)
hybrid system was developed, and theoretical models were proposed
to evaluate the system ’se fficiency. They demonstrated that the poly-
crystalline silicon thin- film PV cell was suitable for concentrating
PV/TE hybrid systems through optimizing the convective heat trans-
fer coef ficient and concentrating ratio ( Zhang et al. 2014 ). Zhou
et al. ( 2015 ) examined how the temperature distribution on the cell
layer and the module ’s thickness direction, and how temperature dis-
tribution on the photocell based on the finite-element simulation
technique, affect the photocell ’s performance. They developed a so-
lution using a better data management system to improve the predic-tion of energy production and determine the anomalies of speci fic
cells and their characteristics. Chiou and Huang ( 2015 ) developed a
high-dynamic –range imaging-based data acquisition system for the
luminous exterior environment of a daylight simulation model.
Objectives and Scope
The goals of this study are, first, to develop an understanding of
how outside air temperature in fluences electricty consumption of
high-performance buildings, and second, to understand how outside
air temperature affects the electricty generation of solar panels. The
project relies on real-time data for the analyses. There are threeresearch hypotheses. The first hypothesis assumes there is no rela-
tionship between outside temperature and electricity consumption
of high-performance buildings. It is assumed that a high-performancebuilding will reduce heat transfer through its envelop; thus, its
indoor air temperature will not be affected by the outdoor air tem-
perature. The second hypothesis assumes that there is no relation-ship between the heat index and electricity consumption. It is
assumed that low humidity in Arizona (even when there is a high
temperature) has little effect on electricity consumed by the build-
ings. The third and final hypothesis assumes that excessive heat
negatively impacts the energy generation of solar cells in Arizona.
It is assumed that as the outdoor air temperature surpasses 30°C,
the ef ficiency of solar panels will start to diminish.
Research Methodology
Research Framework
Real-time data were gathered from four research buildings located
at ASU (Fig. 2). These buildings are all U.S. Green Building
Council ’s Leadership in Energy and Environmental Design
(USGBC LEED) certi fied. Table 2describes each building accord-
ing to its electricity use, area, and typology.
The four buildings shared a single substation in the university
that is only connected to the four buildings. This provides clear in-formation and allows the research team to analyze the technical
losses during distribution and transmission as well as track the
energy supplied to and consumed by the buildings. The research
team was able to collect data about the difference between total
energy supply and actual energy consumption. The project team
avoided using buildings that are affected by the school ’s semester,
in which the numbers of students vary throughout the day and year.As a result, they avoided using buildings that were affected by var-
iations, such as classrooms and faculty of fices. The selected
Fig. 2. Selected research buildings (images courtesy of Arizona State University): (a) Bio Design Institute A; (b) Bio Design Institute B; (c)
Interdisciplinary Science and Technology 1; (d) Interdisciplinary Science and Technology 4
Table 2. Research Buildings ’Electricity Use, Area, and Typology
BuildingsAnnual electricity
use (KW · h) Net area (m2) (KW · h/m2) Labs area (m2) Office area (m2) Class area (m2) Labs (%) Office (%) Class (%)
BDAa4944724.7 7,923 624.10 4,816 2,365 0 60.7 29.8 0.0
BDBb5149524.4 7,520 684.78 3,011 1,908 0 40.0 25.3 0.0
ISTB1c8164566.8 8,050 1014.23 4,158 1,771 0 51.6 22.0 0.0
ISTB4d8104761.8 14,858 545.48 7,563 3,819 357 50.9 25.7 2.4
aBio Design Institute A (LEED Gold).
bBio Design Institute B (LEED Platinum).
cInterdisciplinary Science and Technology 1 (LEED Gold).
dInterdisciplinary Science and Technology 4 (LEED Gold).
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buildings that were mostly laboratories and of fices and that were
occupied year round.
The selected buildings relied on the electricity supplied by a local
utility company and solar panel installed on campus for most of their
equipment. Natural gas was the energy source for heating and cook-ing. The PV cells used on the site are polycrystalline silicon panels.
Their capacities range from 205 to 240 W with ef ficiency rates from
23.7 to 25.4%. Interdisciplinary Science and Technology 4 is the
only building that did not have any PV cells installed on its premise.
Data Collection
Real-time and building-speci fic data on energy supply and con-
sumption as well as solar energy production were collected from
the four buildings. The university ’s weather station provided the
historical real-time weather data (e.g., outside temperature and heat
index), and the National Oceanic and Atmosphere Administration
(NOAA) provided real-time daily ultraviolet (UV) index data. Data
between 2011 and 2014 were used for the analysis. The research
team used the Statistical Package for the Social Science ( SPSS ) soft-
ware to perform the statistical analyses, and Fig. 3illustrates the
flowchart of the methodology. The next sections cover the data
characterization, statistical methods, and presented the results.
Data Characterization
The data from different sources were treated before the analysis.
The data, including energy consumption, renewable energy (solar)
production, and the weather data, such as outside temperature and
heat index, were assessed to ensure their consistency. Data sets withmissing data were aligned accordingly before the analyses. For
example, data were lost when ASU changed/overhauled the meters
and energy management system in 2013, and all data from these
lostdays were removed from the analysis. The analyses were also
based on previously developed methodologies used in past studies.The weather data were separated into CDDs and HDDs, and the
threshold value for the city of Phoenix was 24°C ( Akbari et al.
1992 ). Days in which the average temperature was higher than 24°
C were grouped as CDDs, and days in which the average tempera-
ture was lower than 24°C were grouped as HDDs. However, the
HDDs were excluded from the analyses because none of the build-
ings used electricity for heating.
Data Analysis
Statistical analyses were conducted to examine the following steps
and prove these hypotheses: (1) the signi ficance of the relationship
between electricity consumption and outdoor air temperature, (2)
the signi ficance of the relationship between electricity consumption
and heat index, and (3) the ef ficiency of the PV cells affected by
atmospheric temperature. The statistical tests include the following:
1. Correlation was used between outdoor air temperature and
energy consumption of CDDs.
2. Regression analyses were used to determine whether
a. Electricity consumption in CDDs is a dependent variable
between air temperature and energy consumption,
b. Outside temperature in CDDs is the first independent vari-
able, and
c. The heat index in CDDs is the second independent
variable.
3. Descriptive statistics were used to compare solar panel ef fi-
ciency in HDDs with CDDs.
Relationships between Electricity Consumption, Heat
Index, and Outside Temperature
The research team performed a correlation analysis between outside
temperature and electricity consumption while ignoring humidity.
The analysis showed that outside temperature and electricity
Fig. 3. Methodology flowchart
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consumption were not correlated. The calculated R2values, shown
in Figs. 4–7, were very low (each figure corresponds to a building).
The few outlines were not included due to the large number of data
points. Humidity affects how humans feel, and higher humidity
leads to a warmer and more uncomfortable environment. Heat indexis the most common way to factor humidity into temperature (appa-
rent temperature −temperature that human feels) ( NWS 2015 ). The
ambient temperature was also used as an independent variable,whereas the energy consumption was the dependent variable of
the regression equation. The outside temperature and the heatindex are highly correlated with each other in Arizona because
the relative humidity in the Phoenix metropolitan area rarely sur-passes 40%. A separate regression analysis exercise is performed
on each independent variable rather than a multivariate regression
analysis exercise. The results of both regression analyses are pre-sented in Table 3.
The results in Table 3show that outside temperature has little to
no relationship with electricity consumption for the four buildings.Similarly, the heat index is not correlated to electricity consumption.
Therefore, humidity is not a signi ficant factor; thus, it has no
Fig. 4. Outside temperature versus energy consumption (Bio Design Institute A)
Fig. 5. Outside temperature versus energy consumption (Bio Design Institute B)
Fig. 6. Outside temperature versus energy consumption (Interdisciplinary Science and Technology 1)
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relationship with electricity demand. The low p-values presented
in Table 3confirm the high signi ficance of the performed analy-
ses. These results could be a consequence of the high-quality
design and high-ef ficiency features that are common to the whole
sample. The building envelopes are effectively providing thermalisolation to the indoor environment. Therefore, the cooling loads
are not in fluenced by the outside temperature but remain rela-
tively constant. In addition, the relative humidity is constant
throughout the year in the Phoenix metropolitan area, and the heat
index has little to no in flu e n c eo ne n e r g yc o n s u m p t i o n( e x c e p t
during the short monsoon season).
Outside Temperature versus Solar Energy Production
Approximately 50% of the university ’s annual energy consumption
is produced by the solar systems installed on campus, and three of
the four sampled buildings use solar energy. The performance of
PV cells is in fluenced by the weather, such as outdoor air tempera-
ture, wind speed, and solar radiation. The output of solar cells
(energy production) depends directly on the UV radiation index that
the National Weather Service (NWS) and the U.S. EPA developedin 1994. The index absolute values vary from 1 (low) to 11 ț
(extreme). The NOAA provided online daily UV index data for the
entire country, including the city of Phoenix. Instead of calculating
the cell ef ficiency for each period, the research team analyzed the
ratio between the energy produced and the UV index. The team
then compared the kilowatts produced per unit of UV index for
CDDs and HDDs. During CDDs, the median UV index in Phoenixis 11.20 (= extreme), whereas during HDDs this value is 4.78
(= moderate). Table 4shows the results of this comparison.
The previously mentioned simple analysis suggests that there is
a marked difference between the ratios, as seen in hypotheses (1)
and (2), i.e., CDDs and HDDs. The kilowatts produced per UV
index unit during HDDs range from 1.62 to 1.89 times the same ra-
tio during CDDs. This comparison indicates that the solar panels
can produce almost twice as much energy with the same amount of
UV radiation when the weather is cooler than when it is warmer. Inother words, the ef ficiency of PV cells is notably reduced in the
warmer days (above 24°C in Phoenix). These results match the liter-
ature on the subject, in which authors stated that higher tempera-
tures have an adverse impact on the solar cells ’efficiency ( Wurfel
2005 ;Skoplaki and Palyvos 2009a ;Skoplaki et al. 2008 ;Alonso
Garcia and Balenzategui 2004 ;Malik et al. 2010 ;Skoplaki and
Palyvos 2009b ).Conclusions
This paper analyzed the relationship between outside temperature
and electricity consumption and production in four high-performanceresearch buildings from ASU. Daily real-time data were gatheredbetween 2011 and 2014 and used for the analyses. Correlation
analyses, descriptive statistics, and regression analyses were used
to analyze the data. The independent variables studied in this paperincluded outside temperature and heat index. The dependent varia-bles were electricity consumption and solar energy production.Unlike prior research, the study used real-time data to af firm the
results from prior research. The findings include the following:
1. The analysis found no correlation between outside temperature
and electricity consumption for all four facilities. The build-ings ’high-ef ficient envelope is a contributing factor to the low
correlation. These buildings had substantial levels of insulationand energy ef ficiency; thus, the outside air temperature does
not affect the indoor air temperature. Therefore, they probably
have become independent of outside temperature variations.
2. Regression analyses indicated that the heat index also had no
correlation with electricity consumption in the buildings; thus,humidity has no correlation with electricity demand for thesamples.
3. The research af firms the results from prior studies that the
increasing outdoor air temperature reduces solar panel perform-ance. The study showed that the ef ficiency was reduced by half
between CDDs and HDDs. This impact also varies according
Fig. 7. Outside temperature versus energy consumption (Interdisciplinary Science and Technology 4)
Table 3. Results of Regression Analyses between Energy Consumption,
Heat Index, and Outside Temperature (2011 –2014)
Building Independent variables R2(%) P-value
ISTB1aHeat index 1.60 7.2789 /C210−109
Outside temperature 2.76 2.4513 /C210−125
ISTB4bHeat index 0.15 0.000115011
Outside temperature 0.02 0.003100986
BDBcHeat index 0.54 1.13729 /C210−15
Outside temperature 0.02 4.50912 /C210−27
BDAdHeat index 0.37 1.13729 /C210−15
Outside temperature 1.15 1.5359 /C210−134
aInterdisciplinary Science and Technology 1 (LEED Gold).
bInterdisciplinary Science and Technology 4 (LEED Gold).
cBio Design Institute B (LEED Platinum).
dBio Design Institute A (LEED Gold).
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to the solar panels ’manufacturing materials, wind speed, and
solar radiation. This study found that the ef ficiency of polycrys-
talline silicon solar panels in Arizona was reduced during
CDDs when the ambient temperatures are higher.
Discussion
This study was based on real-time data and existing buildings, andsuggested that the electricity consumption in high-performancebuildings is independent of outside air temperature. There are some
practical implications of this study. First, the design of an ef ficient
envelope signi ficantly prevents the heat transfer from the outside air
to the building ’s indoor environment. Second, through an ef ficient
envelope design, the building owners would have a more constantelectricity consumption, which means more reliable energy bills
during the year. Third, once the high-performance buildings
become the majority of the constructions in a given location, theenergy concessionaires will have a more stable demand to supply.
Finally, high-performance buildings are not subject to the adverse
effects of climate change on their electricity consumption. On the
other hand, the analyses showed that that the solar panels suffer
from ef ficiency losses during the CDDs. Therefore, rising tempera-
ture caused by climate change is a threat to renewable energy pro-
duction from PV cells.
The study is limited in its research because it solely relies on
local and unique factors, such as temperature and humidity.
Therefore, the presented results cannot be generalized for otherparts of the country or region. Further research is needed to assess
the relationship between the analyzed variables in other climate and
microclimate zones. Also, temperature and humidity are not theonly variables related to weather conditions. Researchers should
also take into consideration other factors, such as wind speed and
solar radiation, for future studies.
Also, the typology of the buildings must be addressed before
comparing the results. The same methodology applied to a class-
room building in the same university may lead to different out-
comes. Analyzing the users ’behavior is crucial to understanding
this variation. Therefore, researchers should consider filtering the
occupancy factor and its impact on energy consumption in build-
ings throughout the year (which does not happen with the samplestudied in this paper). Additionally, energy demands of large
commercial buildings are expected to differ from smaller build-
ings, whereas energy use controllability in fluences the amount of
energy consumed. The energy demand of a small house is more
likely to be sensitive to weather factors. Conversely, larger com-mercial buildings have their indoor thermal conditions signi fi-
cantly in fluenced by internal factors, such as the occupants and
appliances that generate heat. This study only analyzed high-performance buildings, and these buildings achieved high-level
certification on energy ef ficiency. For further research, the authors rec-
ommend assessing the relationship b etween electricity consumptionand heat index for noncerti fied, average-ef ficient buildings. This
study will test the hypothesis that an ef ficient envelope design is
the reason for the lack of correlation between outside temperature
and electricity consumption in high-performance buildings.
Acknowledgments
This work was conducted during a scholarship supported by the
International Cooperation Program CAPES/LASPAU at ASUfinanced by the CAPES –Brazilian Federal Agency for Support and
Evaluation of Graduate Education within the Ministry of Education
of Brazil. Their contribution is gratefully acknowledged.
References
Akbari, H., Davis, S., Dorsano, S., Huang, J., and Winnett, S. (1992).
Cooling our communities: A guidebook on tree planting and light-colored surfacing , EPA, Washington, DC.
Alonso Garcia, M. C., and Balenzategui, J. L. (2004). “Estimation of photo-
voltaic module yearly temperature and performance based on nominaloperation cell temperature calculations. ”Renewable Energy , 29(12),
1997 –2010.
ASHRAE (American Society of Heating, Refrigerating and Air-
Conditioning Engineers). (2004). “Energy standard for buildings except
low-rise residential buildings. ”ASHRAE Standard 90.1 , Atlanta.
Asimakopoulos, D. A., et al. (2012). “Modelling the energy demand projec-
tion of the building sector in Greece in the 21st century. ”Energy Build. ,
49, 488 –498.
Ayyash, S., Salman, M., and Al-Ha fi, N. (1985). “Modelling the impact of
temperature on summer electricity consumption in Kuwait. ”Energy ,
10(8), 941 –949.
Berger, T., et al. (2014). “Impacts of climate change upon cooling and heat-
ing energy demand of of fice buildings in Vienna. ”Energy Build. ,8 0 ,
517–530.
Bessec, M., and Fouquau, J. (2008). “The non-linear link between electric-
ity consumption and temperature in Europe: A threshold panelapproach. ”Energy Econ. , 30(5), 2705 –2721.
Bigano, A., Borsello, F., and Marano, G. (2006). Energy demand and tem-
perature: A dynamic panel analysis , Fondazione Eni Enrico Mattei,
Milan, Italy.
Carlo, J., and Lamberts, R. (2008). “Development of envelope ef ficiency
labels for commercial buildings: Effect of different variables on electric-ity consumption. ”Energy Build. , 40(11), 2002 –2008.
Chiou, Y. S., and Huang, P. C. (2015). “An HDRi-based data acquisition
system for the exterior luminous environment in the daylight simulationmodel. ”Sol. Energy , 111, 104 –117.
Coskun, C., Ertük, M., Oktay, Z., and Hepbasli, A. (2014). “An e w
approach to determine the outdoor temperature distributions for buildingenergy calculations. ”Energy Convers. Manage. , 78, 165 –172.
Dodoo, A., Gustavsson, L., and Bonakdar, F. (2014). “Effects of future cli-
mate change scenarios on overheating risk and primary energy use for
Swedish residential buildings. ”Energy Procedia , 61, 1179 –1182.Table 4. Results of the Comparison between Energy Produced per UV Index Unit in CDDs and HDDs (2011 –2014)
BuildingCDDsProduction/UV
index (1) (kw)HDDsProduction/UV
index (2) (kW) Ratio (2)/(1) Production (kW) UV Index Production (kW) UV Index
ISTB1a321.96 11.20 28.73 259.49 4.78 54.28 1.89
BDAb439.26 11.20 39.20 308.89 4.78 64.62 1.65
BDBc381.83 11.20 34.07 264.33 4.78 55.30 1.62
aInterdisciplinary Science and Technology 1 (LEED Gold).
bBio Design Institute A (LEED Gold).
cBio Design Institute B (LEED Platinum).
© ASCE C4017002-10 J. Archit. Eng.
J. Archit. Eng., -1–1
Downloaded from ascelibrary.org by Arizona State Univ on 03/11/17. Copyright ASCE. For personal use only; all rights reserved.

Dubey, S., Sarvaiya, J. N., and Seshadri, B. (2013). “Temperature depend-
ent photovoltaic (PV) ef ficiency and its effect on PV production in the
world –A review. ”Energy Procedia , 33, 311 –321.
Fan, J. L., Tang, B. J., Yu, H., Hou, Y. B., and Wei, Y. M. (2015). “Impact
of climatic factors on monthly electricity consumption of China ’ss e c –
tors. ”Nat. Hazards , 75(2), 2027 –2037.
Fang, Z., Li, N., Li, B., Luo, G., and Huang, Y. (2014). “The effect of build-
ing envelope insulation on cooling energy consumption in summer. ”
Energy Build. , 77, 197 –205.
Frank, T. (2005). “Climate change impacts on building heating and cooling
energy demand in Switzerland. ”Energy Build. , 37(11), 1175 –1185.
Goyal, S., Ingley, H. A., and Barooah, P. (2013). “Occupancy-based zone-
climate control for energy-ef ficient buildings: Complexity vs. perform-
ance. ”Appl. Energy , 106, 209 –221.
Hart, M., and de Dear, R. (2004). “Weather sensitivity in household appli-
ance energy end-use. ”Energy Build. , 36(2), 161 –174.
He, J., Hoyano, A., and Asawa, T. (2009). “A numerical simulation tool for
predicting the impact of outdoor thermal environment on buildingenergy performance. ”Appl. Energy , 86, 1596 –1605.
Henley, A., and Peirson, J. (1997). “Non-linearities in electricity demands
and temperature: Parametric versus non-parametric methods. ”Oxford
bulletin of economics and statistics , Wiley, Oxford, U.K.
Kalvelage, K., Passe, U., Rabideau, S., and Takle, E. S. (2014). “Changing
climate: The effects on energy demand and human comfort. ”Energy
Build. , 76, 373 –380.
Kibert, C. J. (2013). Sustainable construction: Green building design and
delivery , 3rd Ed., John Wiley &Sons, Hoboken, NJ.
Koehl, M., Heck, M., and Wiesmeier, S. (2012). “Modelling of conditions
for accelerated lifetime testing of humidity impact on PV-modules based
on monitoring of climatic data. ”Sol. Energy Mater. Sol. Cells , 99,
282–291.
Koehl, M., Heck, M., Wiesmeier, S., and Wirth, J. (2011). “Modeling of the
nominal operating cell temperature based on outdoor weathering. ”Sol.
Energy Mater. Sol. Cells , 95(7), 1638 –1646.
Kolokotroni, M., Ren, X., Davies, M., and Mavrogianni, A. (2012).
“London ’s urban heat island: Impact on current and future energy con-
sumption in of fice buildings. ”Energy and Build. , 47, 302 –311.
Lee, C. C., and Chiu, Y. B. (2011). “Electricity demand elasticities and tem-
perature: Evidence from panel smooth transition regression with instru-
mental variable approach. ”Energy Econ. , 33(5), 896 –902.
Lin, G., and Claridge, D. E. (2015). “A temperature-based approach to detect
abnormal building energy consumption. ”Energy Build. , 93, 110 –118.
Ma, Z., Li, H., Sun, Q., Wang, C., Yan, A., and Starfelt, F. (2014).
“Statistical analysis of energy consumption patterns on the heat demand
of buildings in district heating systems. ”Energy Build. , 85, 464 –472.
Malik, A. Q., Ming, L. C., Sheng, T. K., and Blundell, M. (2010).
“Influence of temperature on the performance of photovoltaic polycrys-
talline silicon module in the Bruneian climate. ”ASEAN J. Sci. Technol.
Dev., 26(2), 61 –72.
McCarthy, J. J. (2001). Climate change 2001: Impacts, adaption, and vul-
nerability: contribution of Working Group II to the third assessment
report of the Intergovernmental Panel on Climate Change , Cambridge
University Press, Cambridge, U.K.
Mohareb, E. A., Kennedy, C. A., Danny Harvey, L. D., and Pressnail, K. D.
(2011). “Decoupling of building energy use and climate. ”Energy Build. ,
43(10), 2961 –2963.
Moral-Carcedo, J., and P /C19erez-García, J. (2015). “Temperature effects on
firms’electricity demand: An analysis of sectorial differences in Spain. ”
Appl. Energy , 142, 407 –425.
Moral-Carcedo, J., and Vic /C19ens-Otero, J. (2005). “Modelling the non-linear
response of Spanish electricity demand to temperature variations. ”
Energy Econ. , 27(3), 477 –494.
NWS (National Weather Service). (2015). “What is heat index? ”hhttp://
www.srh.noaa.gov/ama/?n=heatindex i(Jul. 30, 2015).
Olonscheck, M., Holsten, A., and Kropp, J. P. (2011). “Heating and cooling
energy demand and related emissions of the German residential buildingstock under climate change. ”Energy Policy , 39(9), 4795 –4806.
Radhi, H., and Sharples, S. (2013). “Quantifying the domestic electricity
consumption for air-conditioning due to urban heat islands in hot aridregions. ”Appl. Energy , 112, 371 –380.Sailor, D. J. (2001). “Relating residential and commercial sector electricity
loads to climate –evaluating state level sensitivities and vulnerabilities. ”
Energy , 26(7), 645 –657.
Santamouris, M., Cartalis, C., Synnefa, A., and Kolokotsa, D. (2014). “On
the impact of urban heat island and global warming on the powerdemand and electricity consumption of buildings –A review. ”Energy
Build. , 98, 119 –124.
Sathaye, J. A., et al. (2013). “Estimating impacts of warming temperatures
on California ’s electricity system. ”Global Environ. Change , 23(2),
499–511.
Schwingshackl, C., et al. (2013). “Wind effect on PV module temperature:
Analysis of different techniques for an accurate estimation. ”Energy
Procedia , 40, 77 –86.
Scott, M. J., Wrench, L. E., and Hadley, D. L. (2007). “Effects of climate
change on commercial building energy demand. ”Energy Sources ,
16(3), 317 –332.
Seppänen, O., and Kurnitski, J. (2009). “Moisture control and ventilation. ”
WHO guidelines for indoor air quality: dampness and mould , World
Health Organization, Geneva, 31 –
62.
Signor, R., Westphal, F. S., and Lamberts R. (2001). “Regression analysis
of electric energy consumption and architectural variables of condi-tioned commercial buildings in 14 Brazilian cities. ”Proc., Seventh Int.
IBPSA Conf. , Organizing Committee of Building Simulation '01,
College Station, TX, 1373 –1380.
Skoplaki, E., Boudouvis, A. G., and Palyvos, J. A. (2008). “A simple corre-
lation for the operating temperature of photovoltaic modules of arbitrarymounting. ”Sol. Energy Mater. Sol. Cells , 92(11), 1393 –1402.
Skoplaki, E., and Palyvos, J. A. (2009a). “On the temperature dependence
of photovoltaic module electrical performance: A review of ef ficiency/
power correlations. ”Sol. Energy , 83(5), 614 –624.
Skoplaki, E., and Palyvos, J. A. (2009b). “Operating temperature of photo-
voltaic modules: A survey of pertinent correlations. ”Renewable
Energy , 34(1), 23 –29.
SPSS (Statistical Package for the Social Science) [Computer software].
IBM, Armonk, NY.
U.S. DOE. (2011). Buildings energy data book , Washington DC.
U.S. DOE. (2013). “U.S. Energy sector vulnerabilities to climate change
and extreme weather. ”hhttp://energy.gov/sites/prod/ files/2013/07/f2/
20130710-Energy-Sector-Vulnerabilities-Report.pdf i(Jan. 17, 2015).
U.S. Green Building Council. (2015). “Benefits of green building. ”hhttp://
www.usgbc.org/articles/green-building-facts i(Feb. 23, 2015).
Wan, K. W., Li, D. H. W., Pan, W., and Lam, J. C. (2012). “Impact of cli-
mate change on building energy use in different climate zones and miti-gation and adaptation implications. ”Appl. Energy , 97, 274 –282.
Wang, H., and Chen, Q. (2014). “Impact of climate change heating and
cooling energy use in buildings in the United States. ”Energy Build. ,8 2 ,
428–436.
Wangpattarapong, K., Maneewan, S., Ketjoy, N., and Rakwichian, W.
(2008). “The impacts of climatic and economic factors on residential
electricity consumption of Bangkok Metropolis. ”Energy Build. , 40(8),
1419 –
1425.
Wind finder. (2015). “Wind statistics and weather conditions: Phoenix Sky
Harbor. November. ”hhttp://www.wind finder.com/windstatistics/phoenix
_sky_harbor i(Jan. 3, 2016).
Wong, N. H., Jusuf, S. K., Sya fi, N. I., and Li, W. H. (2013). “Mitigation
methods of climate change impact on the cooling load of public residen-tial buildings in Singapore. ”J. Archit. Eng. ,10.1061/(ASCE)AE.1943
-5568.0000124 ,1 4 7 –155.
Wurfel, P. (2005). Physics of solar cells: From principles to new concepts ,
Wiley-VCH, Weinheim, Germany.
Xu, P. (2009). Effects of global climate changes on building energy con-
sumption and its implications on building energy codes and policy inCalifornia: PIER final project report , California Energy Commission,
Sacramento, CA.
Yahhoobian, N., and Kleissl J. (2012). “An indoor-outdoor building energy
simulator to study urban modi fications effects on building energy use –
Model description and validation. ”Energy Build. , 54, 407 –417.
Yang, C. S., and Heinsohn, P. A. (2007). Sampling and analysis of indoor
microorganisms , John Wiley &Sons, Hoboken, NJ.
© ASCE C4017002-11 J. Archit. Eng.
J. Archit. Eng., -1–1
Downloaded from ascelibrary.org by Arizona State Univ on 03/11/17. Copyright ASCE. For personal use only; all rights reserved.

Yu, F. W., Chan, K. T., and Sit, R. K. Y. (2012). “Climatic in fluence on the
design and operation of chiller systems serving of fice buildings in a sub-
tropical climate. ”Energy Build. , 55, 500 –507.
Zhang, J., Xuan, Y., and Yang, L. (2014). “Performance estima-
tion of photovoltaic-thermoelectric hybrid systems. ”Energy , 78,
895–903.Zhou, J., Yi, Q., Wang, Y., and Ye, Z. (2015). “Temperature distribution of
photovoltaic module based on finite element simulation. ”Sol. Energy ,
111, 97 –103.
Zhou, Y., et al. (2014). “Modeling the effect of climate change on U.S.
state-level buildings energy demands in an integrated assessment frame-work. ”Appl. Energy , 113, 1077 –1088.
© ASCE C4017002-12 J. Archit. Eng.
J. Archit. Eng., -1–1
Downloaded from ascelibrary.org by Arizona State Univ on 03/11/17. Copyright ASCE. For personal use only; all rights reserved.
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