Energies 2019, 12, 4783; doi:10.3390/en12244783 www.mdpi.com/journal/energies
Factors Affecting Energy Performance of Large-Scale
Office Buildings: Analysis of Benchmarking Data
from New York City and Chicago
, SangTae No
* and Qamar Alhadidi
Department of Architecture, School of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul
02841, Korea; firstname.lastname@example.org
Department of Architecture, Korea National University of Transportation, 50 Daehak-ro, Geomdan-ri,
Daesowon-myeon, Chungju-si, Chungcheongbuk-do 27469, Korea
Department of Architecture, School of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul
02841, Korea; email@example.com
* Correspondence: firstname.lastname@example.org; Tel.: +82-43-841-5204
Received: 11 November 2019; Accepted: 12 December 2019; Published: 15 December 2019
Abstract: Buildings in high-income, industrialized cities are responsible for more than 50% of global
energy consumption; consequently, many developed cities have legislated energy benchmarking
and disclosure policies to understand their buildings’ energy-use dynamics better. By utilizing these
benchmarking data and additional information taken from 3D models, this paper presents a
comprehensive analysis of large-scale office buildings located in New York and Chicago, with
respect to their energy use intensity (EUI). To identify the primary factors affecting the EUI,
Spearman’s correlation analysis and multiple variate regression tests were performed on office
buildings over 500,000 ft
) gross floor area. The results showed the number of floors,
construction year, window-to-wall ratio (WWR), and source-to-site ratio statistically significant,
while morphological factors such as the relative compactness and surface-to-volume ratio showed
limited relation to EUI. In New York City, the smallest EUI median was found in the buildings with
20 to 30 floors, and in Chicago, the buildings with 60 floors or more. A higher source-to-site ratio
generally had lower overall EUI in both cities. Despite the high correlation, different kinds of
dependency were found for window-to-wall ratio (WWR) and construction year between NYC and
Chicago. These findings highlight the relative role that each building’s characteristics play
concerning the EUI, depending on the particular building’s typology, scale, and the urban context.
Keywords: energy benchmarking; large-scale office buildings; energy disclosure policy; source
energy use intensity; site energy use intensity
Several decades have passed since the building industry recognized the importance of
monitoring the actual energy use intensity of existing buildings. Greater information and data on
energy consumption of buildings enables owners, operators, and tenants to make informed energy
management decisions. Transparent, timely information can help track performance against goals,
and the collection of general statistical information about buildings’ energy use enables better policy
and program design . For this critical data gathering, an increasingly popular policy, which has
been adopted in many European and US cities, is the requirement that building owners disclose their
annual energy use and benchmark it relative to other buildings . New York and Chicago, the two
subject cities of this research, are among the early adopters of this policy and have accumulated data
Energies 2019, 12, 4783 2 of 16
since 2009 and 2013, respectively, for buildings over 50,000 ft² (4645 m²) [3,4]. Among the various
building typologies of such developed, high-income cities, large-scale office buildings are ubiquitous,
compose a large portion of the overall area, and are considered one of the most energy-intensive. In
Manhattan, in terms of the area sum of space taken, nearly 90% of the overall office building stock is
accounted for by large office buildings over 500,000 ft² (46,452 m²) . Due to their sizeable
quantitative portion compared to various other building groups, understanding the energy-
consumption dynamics and prominent features of these large-scale office buildings are crucial.
Already many types of research are available that deep dive into what the accumulated data tell
us. The prevalence of the energy disclosure policy and worldwide benchmarking data have enabled
diverse research that has revealed attributes and patterns of building energy consumptions; a lot of
this research has focused on instituting rigorous methodologies for analyzing performance patterns
over time or how to take multiple features into account when trying to understand energy use
intensity (EUI) dynamics [6–8]. For New York City, by using a K-means clustering algorithm,
Papadopoulos et al.  showed energy reductions are mostly driven by office buildings, with larger,
newer, and higher-value buildings showing significant improvement in EUI between 2011 and 2016.
Gao and Malkawi  also utilized the clustering method to demonstrate that multidimensional
similarity can be used as a ‘performance typology’ to define building type; this approach is similar to
the traditional one-dimensional use type but is much more comprehensive. In this way, the energy
performance of the buildings considered can be more properly benchmarked. A study utilizing the
UK’s DEC (Display Energy Certificate) data from public schools conducted by UCL (University
College London)  also address the alternative analysis method, which compares the top-down and
bottom-up approaches. They concluded the top-down approach utilizing descriptive statistics and
artificial neural networks (ANN) presents many benefits.
Some research has confirmed widely held beliefs, such as the relation between the building’s use
type and energy use intensity in detail. It was shown that building-use typology is the key
determinant of occupant density and operating hours of a building, which inevitably becomes the
most influential factor affecting the energy use intensity of the whole. For example, even though
multifamily properties significantly outnumber offices in many cities, the office sector is the more
energy-intensive of the two, using 50 percent more energy per square foot than the multifamily
building sector in the case of NYC . Within an identical floor area, the number of occupants in an
office is usually much higher than in residential buildings, hence more energy is used. In addition,
Constantine , who analyzed NYC’s 2010 LL84 data for commercial buildings, found that increased
operating hours and occupant density results in higher EUI, which is a predictable result and aligns
with the empirical data from the Commercial Buildings Energy Consumption Survey (CBECS).
One of the important variables included in this research, which has also been a subject of several
previous studies, is the relationship between building height and energy consumption. Godoy-
Shimizu et al.  studied 611 office buildings in England and Wales, concluding there was a
significant energy use increase for high-rise offices defined as 10 stories or above, compared to low
and mid-rise buildings . Another study from the UK, which compared buildings with six stories
or fewer with buildings with 20 stories or more, concluded the electricity use in the high-rise
buildings was nearly two and a half times greater than in low-rise buildings, and that carbon
emissions were more than doubled when going from ‘low’ to’ high-rise’ . Guthrie  concluded,
based on research from Hong Kong’s 20 commercial office buildings, that tall buildings generally use
more energy. Even though the results appear to be consistent that, the higher the building, the more
energy-intensive it is, these results are limited to very specific contexts and locations. For example, in
NYC and Chicago, buildings with 20 stories could be considered as low or mid-rise, depending on
the location. If we consider relative difference in defining high-rise buildings, the results from
previous research cannot be directly applied to all cities, and further investigation is required.
Meanwhile, other research related to the building morphology of large-scale office buildings
besides the height focuses on hypothetical building geometries and orientations and provides results
from model simulations [13–15]. Many studies have found a strong correlation between the shape of
a commercial building, the relative compactness, the percent glazing, and the building’s total energy
Energies 2019, 12, 4783 3 of 16
use intensity . However, there is limited research discussing the subject in conjunction with the
actual geometries and data from real buildings, let alone studies that look at the relative effect
compared to other factors, such as the construction year and building systems. Even though energy
consumption is becoming one of the key factors in determining a building’s form, many other urban,
financial, and legal factors limit architects from exploring the optimal shape for energy use. When we
attempted to categorize the building geometries from these two cities, quite distinct types were
identifiable, which are quite different from the assumed models used in previous researches. For
example, circular, oval, and triangular buildings with plan aspect ratio beyond 1:3 or with courtyards
were rarely found in this specific group [15,17,18]. Therefore, to understand the morphology of
existing buildings, a different approach may be necessary; our assumptions are further discussed in
detail later in this paper.
Even though several previous studies have offered methodologies to improve how we analyze
the ever-increasing benchmarking data or have informed us of the use patterns of a specific region or
a typology; none have attempted to compare data from a group of buildings that share multiple
attributes, such as the program or size, from more than one city. Based on a comprehensive analysis
of the large-scale office building’s energy disclosure data from these two major US cities, this research
seeks to investigate the common factors that affect the energy use intensity in these highly developed
urban environments. In addition to the disclosure data, this research analyzes the formal attributes,
such as the compactness factors, and it introduces a new morphological variable considering the
vertical distribution of the building area, as well as a ratio between the site and source energy use
2. Materials and Methods
2.1. Data Sources and Preparation
The data for this research are primarily from New York and Chicago’s energy benchmarking
disclosure reports from 2015 to 2018; the reported data include information from prior years of
reporting [19–21]. The disclosure data acquired from both cities’ websites include but are not limited
to the following information: building identification number (BIN), property name, address, primary
and secondary property type, gross floor area, year built, Energy Star score, weather normalized site
EUI, weather normalized source EUI, electricity use, natural gas use, district steam use, and oil fuel
use. Additional data, such as the number of floors, floor-to-area ratio (FAR), construction, and
renovation dates (latest construction dates), were added from various online resources, as indicated
in Table 1. Other physical characteristics, including the compactness factor (CF), relative compactness
(RC) , and morphology types, are generated from the 3D models. Window-to-wall ratios (WWR)
are calculated based on the available plans, elevations, and photos of each selected sample building.
Table 1 summarizes the data sources for each variable.
Table 1. Data source of variables for analysis.
Variable Type New York Chicago
Number of Floors 42floors.com, skyscraper.org
Gross Floor Area
Floor Area Ratio oasisnyc.net Calculated from Chicago Data
Disclosure and Portal
Compactness Factor Calculated from 3D Models1 Calculated from 3D Models 2
Relative Compactness Calculated from 3D Models1 Calculated from 3D Models 2
Window/Wall Ratio Calculated from Google Earth Pro,
Photos, Plans, Elevations
Calculated from Google Earth Pro
Photos, Plans, Elevations
Morphology Type Calculated from 3D Models1 Calculated from 3D Models 2
Consumption Data 1
Source/Site EUI NYC Energy Data Disclosure Chicago Energy Data Disclosure
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Use NYC Energy Data Disclosure Chicago Energy Data Disclosure
Source Site Ratio Calculated from NYC Energy Data
Calculated from Chicago Energy Data
Year Constructed NYC Energy Data Disclosure Chicago Energy Data Disclosure
Recent Renovation Date NYC City DOB Chicago City DOB
Notes: 1 downloaded from NYC Department of City Planning (updated in 2018), open data website; 2
downloaded from The City Project by boscorelli3D (updated August in 2017).
The initial benchmarking data acquired from both cities contained information on
approximately 13,000 buildings in NYC and 7200 buildings in Chicago. This research focuses only on
large-scale buildings used primarily for commercial offices. First, the properties whose primary and
secondary use type was defined as offices with a gross floor area of 500,000 ft² (46,452 m²) or higher
were included. Other reports , as well as the CBEC data—public use microdata file published by
the US Department of Energy  have used this 500,000 ft² (46,452 m²) limit for defining the category
of ‘very large’ buildings. Next, based on the provided source and site EUI, we excluded the top and
bottom 5% outliers, which were approximately above 350 kBTU/ft² (1104 kWh/m²) or below 50
kBTU/ft² (158 kWh/m²) in case of the source EUI. These outliers included a large proportion of
abnormal space use—for example, data centers or storage—and would have affected the average
significantly. The final number of building samples that resulted in NYC was 221, and in Chicago, it
was 106, and almost all were located in the central areas (Figures 1 and 2).
Figure 1. NYC (New York City) office buildings larger than 500,000 ft² (46,452 m²).
Figure 2. Chicago office buildings larger than 500,000 ft2 (46,452 m²).
Also, the total number of reported buildings and the data from those buildings have been
increasing every year between 2014 and 2018, for both cities. To utilize the most consistent data and
maximize the number of samples, we calculated the average EUI for each building from all available
years. In addition, for each building, abnormal EUIs compared to other years were omitted since
these may have been caused by temporary high vacancy rates or construction activities. Other
Energies 2019, 12, 4783 5 of 16
consumption data and the source-to-site ratio included in this paper are also averages of the available,
years as described above. Figure 3 summarizes the process taken for preparing the data.
Figure 3. Summary of data preparation methodology and process.
2.2. Physical Variables
In this section, we describe in detail a few variables that require additional explanations. Based
on a previous literature review, compactness factor (CF) (1) and relative compactness (RC) (2) are
defined as below, and for this research , information is taken from the 3D models.
Building Surface Area
CF Compactness Factor = Building Volume (1)
Vol ume/Surf ace Area Bui ldi ng Surface Area Refer ence Cube
RC Rel a t i ve Compact nes s = =
Volume/ Surface Area Reference Cube Surface Area Bui ldi ng
Even though previous studies present a strong correlation between this compactness aspect of a
building and its energy consumption, those researches looked at buildings that are relatively small
in scale and low in height . For large-scale, high-rise buildings, the shapes concerning how the
area is vertically distributed could have additional advantages or disadvantages for energy
consumption. Hence, for this research, we have added a variable that categorizes the buildings into
• Collection of benchmarking disclosure data for New York and Chicago reported between
the years 2015 and 2018 (data for 2014-2017).
• Office as the primary and secondary property type.
• Gross Floor Area to 500,000 ft² (46,452 m²) or higher.
• Exclude abnormal data, top and bottom 5% (Source EUI above 350 or below 50).
• Eliminate Samples with missing values for Weather Normalized Source EUI and Weather
Normalized Site EUI.
Data Filtering Process
• Number of floors;
• Floor Area Ratio (FAR);
• Initial Construction Date;
• Latest Major Renovation Date.
Additional Data Set 1 (Websites and City's Building Department)
• Building Height;
• Building Exterior Surface Area;
• Morphology Type;
• Compactness Factor (CF);
• Relative Compactness (RC);
• Window to Wall Ratio (WWR).
Additional Data Set 2 (Calculated from 3D Models, Online Resources)
• Combine all data in Excel Sheet;
• Unify units and measurements;
• Average Consumption Data from 2014 to 2017;
• Export data to SPSS for processing and analyzing.
Cleaning and Exporting Data
Energies 2019, 12, 4783 6 of 16
five types representing different forms of vertical distribution found in specific urban locations, as
shown in Figure 4.
T3. Tower on
Podium T4. Stepping
Overall volume is
dispersed L > H
ratio, L < H < 2L
is more than 30%
of the overall
exceeding 120 ft
the lower levels
to higher levels
slender ratio, 2L
Figure 4. Morphology type: types defined to categorize vertical space and volume distribution.
In addition, the source-to-site ratio is another variable introduced in this research which will
inform the relative proportion of energy-source types used for each building. Source energy converts
energy use, considering the primary energy, which accounts for the raw fuel that is burned to create
heat and electricity, such as natural gas or fuel oil. The conversion factor currently used for grid-
purchased electricity in the US is 2.8 compared to 1.05 for gas . Due to the limited data available
regarding the actual MEP systems, we believe this information will inform certain patterns in
conjunction with the quantitative-use data for each energy resource’s consumption.
2.3. Descriptive Data
Table 2 lists a summary of the processed data, which include 221 buildings from NYC and 106
from Chicago. NYC’s median site EUI is 275 (kWh/m²), and its source EUI is 670 (kWh/m²); Chicago’s
median site EUI is 232 (kWh/m²), and its source EUI is 585 (kWh/m²). Compared with the citywide
medians for all offices, these numbers are approximately 10% higher in NYC and almost identical in
Chicago , which implies no strong correlation between the building floor area and energy use
intensity. One compelling aspect when we compare the two cities is that Chicago’s EUIs are lower
than NYC’s (Figure 5), and this is somewhat unexpected, considering the degree-days. Chicago has
a slightly colder climate, with a lower average temperature in winter and similarly in summer.
During the 4 years of data collection, Chicago had approximately 1100 °C longer heating degree days
(HDD) and 340 °C shorter cooling degree days (CDD) . To verify the assumptions, we performed
energy simulation with a generic building mass of 1000 m² by utilizing the Prototype Building Models
provided by the US Department of Energy. The results indicated Chicago was using 7% more energy.
Even though detailed analysis is required for an accurate comparison, it is an industry-wide
understanding that heating requires more energy than cooling. In the US, previous studies have
shown a typical central air conditioner is about four times more energy-efficient than a typical furnace
or boiler . However, Chicago’s reported site and source EUI were lower than NYC’s, which
suggests the superior energy efficiency of buildings in Chicago.
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Table 2. Descriptive statistics of NYC and Chicago.
NYC Variables Descriptive Statistics
Variables Min Max Mean Median
Statistic Std. Error
Number of Floors 5 102 33.64 34.00 13.080 0.606 0.164
Gross Floor Area (m²) 46,475 337,859 93,666 78,540 49,583 1.821 0.164
Floor Area Ratio (%) 2.88 48.08 20.42 20.27 6.11 0.816 0.164
Compactness Factor (CF) 0.072 0.262 0.124 0.121 0.026 1.399 0.164
Relative Compactness (RC) 0.313 0.948 0.75 0.74 0.09 –0.552 0.164
Window Wall Ratio (%) 15 76 41.40 41.00 13.554 0.189 0.164
Source Site Ratio 1.170 5.590 2.41 2.40 0.42 –0.020 0.164
Electricity Intensity (kWh/m²) 0.00 309.46 148.47 144.49 48.62 0.916 0.164
Natural Gas Use (kWh/m² 0.00 700.33 16.31 0.05 55.81 8.988 0.164
District Steam Use (kWh/m²) 0.00 251.08 81.72 69.60 64.51 0.561 0.164
Site EUI (kWh/m²) 118.91 730.04 291.87 275.51 84.46 1.020 0.164
Source EUI (kWh/m²) 327.55 1453.24 685.93 670.71 161.91 0.693 0.164
Chicago Variables Descriptive Statistics
Variables Min Max Mean Median
Number of Floors 6 110 34.99 36.00 15.117 1.379 0.235
Gross Floor Area (m²) 46,574 416,514 107,030 89,151 64,586 2.432 0.235
Floor Area Ratio (%) 3.48 61.93 22.67 22.19 10.79 0.824 0.235
Compactness Factor (CF) 0.068 0.194 0.113 0.109 0.024 0.948 0.235
Relative Compactness (RC) 0.447 0.975 0.78 0.80 0.11 –0.499 0.235
Window Wall Ratio 19 74 44.74 43.00 14.996 0.314 0.235
Source Site Ratio 1.442 3.142 2.63 2.67 0.52 –0.381 0.235
Electricity Intensity (kWh/ft²) 52.74 318.59 165.12 160.10 49.47 0.543 0.235
Natural Gas Use (kBtu/ft²) 0.00 228.83 51.36 4.54 0.235 0.00 228.83
District Steam Use (kBtu/ft²) No use reported for the selected samples
Site EUI (kWh/m²) 137.44 451.66 243.74 232.89 62.27 0.628 0.235
Source EUI (kWh/m²) 270.93 1194.22 619.37 585.04 135.41 1.049 0.235
Other significant differences between the two cities were the proportion of the primary energy
sources used. For example, in Chicago, most of the buildings in the selected samples used electricity
as the primary energy source, reaching almost 70% of the overall consumption and approximately
20% gas; no district steam use was reported. Meanwhile, in NYC, more than 30% of the consumption
relied on the district’s steam, electricity was a little over 50%, but there was very limited natural gas
use (Figure 6). New York City’s district steam system of Con Edison is the largest district heating
system in the Western world and has been serving Manhattan’s large-scale commercial and
residential buildings since 1882 . This may be an essential fact for understanding the
abovementioned unexpected dynamic of the EUI. District steam systems utilize pipes for
distribution, resulting in a significant energy loss in the system, lowering the efficiency of any such
scheme. In modern-day district heating, these schemes have high operating and maintenance costs,
make it difficult to connect to end-users (high/low-pressure interface), and have high thermal losses
. Also, utilizing district steam for heating and other purposes generally limits other choices for
the building’s systems that could have higher efficiency. Lastly, due to the relatively small amount
reported, we have excluded the fuel oil uses from our analysis.
Other morphological data, such as the area, floor area ratio, compactness factor, relative
compactness, and window-to-wall ratio, were found to have a similar median, with less than 10%
difference between the two cities.
It should be noted that benchmarking data generally do not include detailed energy end-use
breakdown (e.g., water heating, lighting, cooling, and heating) information and have inherent
limitations for explaining the exact composition of the total consumptions. However, where possible,
additional explanations are discussed.
Energies 2019, 12, 4783 8 of 16
2.4. Method: Multiple Regression Analysis and Spearman Correlation
First, considering the nonparametric distribution of the data, we carried out Spearman’s rank
correlation analysis, to identify any underlying relations between the variables. Second, multiple
regression analysis  was carried out, using IBM SPSS Statistics 25 to identify the most significant
common factors affecting the energy consumption of the sampled large-scale office buildings in each
city. The common factors were then evaluated in detail, utilizing scatter and box plot diagrams to
determine whether the trends reveal any congruity and to investigate further the potential cause of
the patterns found in the regression models.
The ‘Statistical Package for the Social Sciences’ (SPSS) is a package of statistic programs from
IBM for solving research problems by means of analysis, hypothesis testing, geospatial analysis, and
predictive analytics .
Figure 5. Site and Source energy use intensity comparison between NYC and Chicago.
Figure 6. Electricity, natural gas and district steam use comparison between NYC and Chicago.
3. Results and Discussion
3.1. Correlation Results between Variables
Table 3 presents the result of Spearman’s correlation analysis between the variables. Fairly many
variables show strong statistical correlations, of which some reveal unexpected patterns while others
were obvious and expected. Considering our research focus, we have highlighted only the factors
that are commonly significant in both cities. Overall, number of floors and construction year were the
two factors that most related to other variables. Starting in the early 20th century, we can assume
Energies 2019, 12, 4783 9 of 16
that, as the years progressed, there were taller buildings built with higher FAR and WWR, and the
results below confirm this relationship. One obvious pattern to note is the relationship between the
construction year and compactness factor. In both cities, the negative numbers signify that recently
constructed buildings are more compact in their forms, with smaller surface-area-to-volume ratio,
which reflects the modern trends of maximizing efficiency for the limited available real estate. In
addition, the connections between site-to-source ratio, number of floors, and construction year are
worth further attention. The construction year and source-to-site ratio have a strong positive relation,
which means the newer buildings have a higher source-to-site ratio. The higher the rate, the more the
building’s relative reliance on electricity as its primary fuel increases. Especially in Chicago, the
significance level appears to be exceptionally high compared to NYC. Moreover, the ratio positively
relates to the number of floors in Chicago, while in NYC, a negative relation is found. This indicates
that, in Chicago, taller buildings rely more on electricity, while in NYC, taller buildings rely on other
energy sources. These findings relate to the earlier discussion in the statistical description section of
this paper, and it is evident that NYC’s district steam system is the primary cause of these results.
Table 3. Spearman’s rank correlation coefficients between variables.
New York City Morphology
Number of Floors −0.386** 0.362** 0.115 0.567** 0.104 −0.125 −0.450** −0.229**
Morphology Type −0.300** −0.128 −0.127 −0.183** 0.044 0.272** 0.039
Year Constructed 0.234** 0.165* 0.262** −0.132* −0.035 0.151*
Latest Construction Date
−0.051 0.029 −0.113 −0.098 0.064
Floor Area Ratio (%) 0.046 0.100 −0.169* 0.071
Window Wall Ratio (WWR)
0.032 0.043 −0.060
(CF) Compactness Factor
(RC) Relative Compactness
*. Correlation is significant at the 0.05 level (2-tailed), **. Correlation is significant at the 0.01 level (2-tailed).
Number of Floors −0.327** 0.459** 0.119 0.639** 0.036 −0.397** −0.086 0.233*
Morphology Type −0.138 −0.206* −0.169 −0.002 0.002 0.184 −0.113
Year Constructed −0.027 0.460** 0.307** −0.411** 0.265** 0.727**
Latest Construction Date
0.011 −0.121 0.068 −0.394** 0.154
Floor Area Ratio (%) 0.133 −0.229* 0.253** 0.352**
Window Wall Ratio (WWR)
−0.098 0.289** 0.281**
(CF) Compactness Factor
(RC) Relative Compactness
*. Correlation is significant at the 0.05 level (2-tailed), **. Correlation is significant at the 0.01 level (2-tailed).
3.2. Multiple Regression Analysis
Given the multiple independent variables, we used a multiple linear regression test in SPSS, to
determine the most influential factors affecting the site and source energy use intensity (dependent).
Tables 4 and 5 present the results, which include several variables significant at or above a 95%
confidence level. In both cities, the number of floors, window-to-wall ratio, and source-to-site ratio
were the most significant factors for site EUI. Year constructed was another common factor, with a
confidence level at above 90%. However, the significance level was not as strong for the source EUI.
Even though electricity, gas, and district steam use showed the highest significance, the above factors
are parts that compose the overall EUI and are apparent results.
Energies 2019, 12, 4783 10 of 16
Table 4. NYC multiple regression analysis result.
Weather Normalized Site EUI Weather Normalized Source EUI
R = 0.872 R2 = 0.761 R = 0.799 R2 = 0.639
Coefficients t Sig
Coefficients t Sig.
Number of Floors –0.232 1.552 0.063 * –0.289 0.452 0.056*
Grouped by Number of Floors 0.302 –1.869 0.012 ** 0.343 –1.923 0.019 **
Morphology –0.026 2.523 0.530 –0.036 2.366 0.460
Window Wall Ratio 0.062 –0.629 0.013 ** 0.054 –0.740 0.237
Gross Floor Area 0.106 1.643 0.065 0.165 1.186 0.018 **
Year Constructed –0.089 1.855 0.048 ** –0.070 2.383 0.195
Latest Construction Date 0.051 –1.988 0.188 0.044 –1.300 0.347
Floor Area Ratio (%) –0.038 1.321 0.427 –0.037 0.943 0.530
(CF) Compactness Factor 0.045 –0.796 0.488 0.054 –0.629 0.494
(RC) Relative Compactness 0.050 0.695 0.425 0.067 0.685 0.378
Source Site Ratio –0.251 0.799 0.000 *** 0.176 0.884 0.001 ***
Electricity Intensity 0.439 –5.775 0.000 *** 0.636 3.34 0.000 ***
Natural Gas Use 0.463 11.460 0.000 *** 0.309 13.71 0.000 ***
District Steam Use 0.522 12.047 0.000 *** 0.465 6.633 0.000 ***
*** Significant at the 99% confidence level, ** significant at the 95% confidence level, * significant at
the 90% confidence level.
Table 5. Chicago multiple regression analysis result.
Weather Normalized Site EUI Weather Normalized Source EUI
R = 0.919 R2 = 0.845 R = 0.902 R2 = 0.814
Coefficients t Sig.
Coefficients t Sig
Number of Floors 0.544 –0.188 0.014 ** 1.389 0.158 0.027 **
Grouped by Number of Floors –6.770 1.074 0.018 ** –16.445 1.193 0.059 *
Morphology 1.500 –1.173 0.374 1.639 –1.238 0.649
Window Wall Ratio –3.057 0.742 0.009 ** –5.114 0.353 0.117
Gross Floor Area –8.201 × 10−5 –1.575 0.072 0 –1.145 0.059 *
Year Constructed 0.140 –1.109 0.054 * 0.058 –1.203 0.675
Latest Construction Date 0.079 0.935 0.320 0.094 0.168 0.536
Floor Area Ratio (%) 0.248 0.675 0.365 0.042 0.347 0.937
(CF) Compactness Factor –41.743 0.748 0.739 –284.712 0.055 0.302
(RC) Relative Compactness –72.377 –0.182 0.009 –179.293 –0.541 0.023
Source Site Ratio –97.220 –1.517 0.005 ** –50.714 –1.633 0.009 **
Electricity Intensity 1.094 –7.810 0.005 ** 3.046 –1.770 0.005 **
** significant at the 95% confidence level, * significant at the 90% confidence level.
Interestingly, variables such as the compactness factor (CF) and relative compactness (RC),
which were found to be the most significant factors in some studies utilizing benchmarking data from
the low-rise school buildings in the UK , did not appear significant for this specific group. Overall
floor area and floor area ratio were not found to be significant factors. In addition, the latest
construction date, which indicates the date of the latest major renovations made to each building,
showed no significance, while the initial built year indicated strong relations with the energy
consumption in both cities.
The primary goal of this research was to investigate the prevailing common indicators in two
different cities; to this end, further investigation was carried out for the four factors identified above.
3.2.1. Number of Floors and Energy Use Intensity
Due to the unprecedented increase in high-rise building construction in recent years, a few
studies have examined the relationship between building height and energy consumption. However,
limited research exists for the cities where buildings with 20 floors or above are prevalent. Godoy-
Shimizu et al.  observed energy use and office building heights for buildings in England and
Wales, where they concluded energy use intensifies as the height increases, due to the greater
Energies 2019, 12, 4783 11 of 16
exposure of taller buildings to lower temperatures, stronger winds, and more solar gains. Other
related studies utilizing data from Display Energy Certificates (DEC) and neighborhood density
around London also conclude higher buildings are more energy-intensive . However, high-rise
building heights in European cities are significantly lower if compared with other continents; the
above research categorizes buildings with 10–20 floors as high-rise, which could be categorized as
low-rise buildings in cities like New York and Chicago. Another study based on a hypothetical
parametric model, which analyzes the individual energy use of the different urban components,
suggests energy intensity changes profoundly with height. Rather than a linear relationship, this
study identified that the optimal number of floors are found to be in the range of 7–27, depending on
population and building lifetime .
The regression result for the number of floors in this research shows a robust significance
compared to other variables. In Figure 7, the box and whisker plot diagrams present the energy use
intensity per group (buildings grouped based on ten-floor intervals) for ease of comparison. In NYC,
the lowest median EUI was found for buildings between 20 and 30 floors, and In Chicago, the group
above 60 floors. In both cities, EUI for the buildings with less than 20 floors indicated relatively higher
EUI than buildings of the taller groups. Due to the limited sample numbers in some groups, it may
be premature to generalize the results. However, in contrast to the previous research, the building
height and energy use intensity did not show a linear relationship for large-scale, super-tall
commercial buildings in these highly developed cities. Even though statistical studies have limits in
explaining causes in full detail, this result potentially relates to the distribution of mechanical
systems. It is common for a single technical floor to support between 15–20 floors, either above or
below its location. Technical floor spacing beyond these parameters will increase losses from friction
and gravity forces, which diminishes energy performance and gives rise to ongoing operation and
maintenance issues . Such concentrated mechanical floors are hard to incorporate in lower-height
buildings; hence, these systems get scattered on the roof or in basements, reducing the efficiency of
distribution. Also, the taller building groups include more buildings constructed in recent years, so
more highly efficient systems are incorporated.
Figure 7. Site and source energy use intensity per number of floors.
3.2.2. Construction, Renovation Years, and Energy Use Intensity
Due to the lower glazing ratio and thermal masses created by relatively thick masonry walls,
some studies indicate that the pre-war buildings are generally more energy-efficient than recently
constructed buildings . These construction attributes are similar in both cities. However, in
Chicago, the buildings constructed and renovated since 1980 clearly showed a decline in their site
EUI, even though the source EUI conversion still shows an increase, while in NYC, our result
coincided with previous findings (Figures 8 and 9). This finding warrants attention, as the differences
between the two cities may inform us of the positive factors that affect use intensity. As described in
Energies 2019, 12, 4783 12 of 16
Section 2.3, New York City’s district steam system is the largest in the Western world, serving the
majority of the selected samples in this research . Meanwhile, of the buildings in Chicago’s
sample, approximately 70% rely on electricity, and the rest rely on natural gas, as their fuel source.
Including high-efficiency electrical and gas-powered heat pumps, technological advances for heating
and cooling systems in recent decades could be the primary cause of this result. Due to the high
reliance on district steam, especially for heating, these recent innovative systems may not have been
utilized fully in NYC. Still, in the case of Chicago, the actual site EUI has benefitted from these
advances over time. Currently, the site-to-source conversion factor for grid-purchased electricity in
the US is 2.8, which is exceptionally high compared to other resources. However, many cleaner
options are being considered to substitute for coal, which is the primary energy source used to create
the majority of the electricity, and the site EUI for the electricity-dependent buildings will possibly
get lower in the future. Based on this result, we could argue the installation of high-efficiency
electrically dependent systems over time may result in better performing buildings than by using
Figure 8. Site and source energy use intensity per construction year.
Figure 9. Site and source energy use intensity per latest construction date including the major
3.2.3. Window-to-Wall Ratio and Energy Use Intensity
Energies 2019, 12, 4783 13 of 16
The balance between glazing and opaque areas alone has an impact on many aspects of the
energy balance; it influences solar gain (and thus energy use for heating and cooling) and heat loss
(mainly affecting energy use for heating), but it also impacts daylight availability (with implications
on energy use for artificial light) . A study by Ballarini et al.  on retrofitted office buildings
enveloped with reduced glazing area showed significant improvements in the thermal performance
and comfort; however, a daylighting reduction occurred, with a consequent higher electricity
demand for lighting (36%). Consequently, many previous types of research have suggested the
optimum ratio depends on climate but ranges from 30% to 50% in the case of continental climate
locations similar to NYC and Chicago . In both cities, the median WWR was approximately 40%,
even though many buildings had a much higher or lower percentage. Our regression model indicated
a significant relationship between the EUI and WWR. Without detailed information on the thermal
values or the orientation of each façade, the ratio itself cannot provide an accurate evaluation of the
effects. However, as presented in Figure 10, we can see the trends in NYC and Chicago were quite
different. The site EUI increased in NYC, while Chicago showed a decrease with higher WWR. The
recently constructed buildings have a higher WWR in general (Figure 11); these results imply that
Chicago’s newly built buildings are much more energy-efficient, even overcoming the disadvantages
of the higher glazing ratio. Again, we speculate that the cause of the difference is due to the limits of
the district steam widely used in NYC, while Chicago has been installing high-performance systems,
taking advantage of technological advances in heating and cooling systems.
Figure 10. Site and source energy use intensity per window-to-wall ratio.
Figure 11. Relation between Construction Year and Window to Wall Ratio.
3.2.4. Source–Site Ratio and Energy Use Intensity
In order to assess the relative efficiencies of buildings with varying proportions of primary and
secondary energy consumption, the industry recommends converting these two types of energy into
equivalent units of raw fuel consumed, to generate one unit of energy consumed on-site. Primary
Energies 2019, 12, 4783 14 of 16
energy is the raw fuel that is burned to create heat and electricity, such as natural gas or fuel oil used
in on-site generation. Secondary energy is the energy product (heat or electricity) created from a raw
fuel, such as electricity purchased from the grid or heat received from a district steam system .
Site energy is the amount of heat and electricity consumed by a building, as reflected in utility
bills, whereas source energy represents the total amount of raw fuel that is required to operate the
building. It incorporates all transmission, delivery, and production losses. By considering all energy
use, the data provide a complete assessment of the energy efficiency of a building. As noted by many,
accounting for the source energy is vital for giving an accurate picture of a building’s energy
consumption . However, the majority of previous research utilizing the benchmarking data has
not addressed the relations of the two energy-use types [6,9]. In this research, we included both site
and source EUI, as well as the ratio in between as one of the test variables.
Electricity from the grid, in most countries, is still considered to be one of the least-efficient fuel
types; in the US, the conversion factor from site to the source is 2.8 compared to natural gas, at 1.05,
and steam or hot water at 1.2. Therefore, a higher source-to-site ratio implies the building’s primary
systems heavily relying on electricity rather than other resources, such as gas and district steam.
Based on the benchmarking data, we calculated the site-to-source ratio for the selected samples. For
both New York and Chicago, this ratio indicated the highest significance in predicting the source and
site EUI. However, the trends from the two cities were not uniform, as shown in Figure 12. In NYC,
a higher ratio means both the source and site EUI decreases, while in Chicago, a higher ratio means
the source EUI increases but the site EUI decreases. In addition, in NYC, many buildings’ ratios are
concentrated between 2–3, while the ratios for buildings in Chicago are concentrated above 3. From
the results, it is clear that the more the building relies on electricity, the less the overall site EUI
compared to other energy resources. Chicago’s high dependency on electricity compared to NYC also
warrants attention; this could be the primary cause for the lower EUIs.
Figure 12. Site and source energy use intensity per source site ratio.
This research evaluated the source and site EUI of 327 large-scale office buildings with a gross
floor area exceeding 500,000 ft² (46,452 m²), located in New York City and Chicago. Statistical tests
were carried out to identify the distinctive trends and predictors for energy consumption.
The analysis results presented in this work identify four statistically significant key common
factors affecting the building energy use intensity: number of floors, construction year, window-to-
wall ratio, and source-to-site ratio.
Contrary to the findings of many previous studies that found the relation between building
height and energy consumption to be linear, in NYC, the lowest EUI median was found in buildings
with 20–30 floors, and in Chicago, the buildings with more than 60 floors. For construction year and
Energies 2019, 12, 4783 15 of 16
window-to-wall ratio, our results also go beyond previous research, showing that, for example, in
Chicago, the newer buildings with higher WWR used less energy. The ratio between the source and
site energy use intensity was one of the unique variables we have introduced to this research, and
with higher ratios, the EUI declined in both cities. Additional, comprehensive analysis is required;
however, the above findings imply that Chicago’s electrical, gas-driven systems could be much more
energy efficient than NYC’s district steam-based systems, outweighing other adverse morphological
and climate factors of large-scale office buildings.
The presented results in this paper should be interpreted as observations of general trends from
the reported benchmarking data and have inherent limitations for identifying exact causes. Therefore,
further work is certainly under consideration to disentangle these complexities through theoretical
simulations and models that consider more-specific building systems, as well as urban contexts.
Author Contributions: Conceptualization, C.W. and S.N.; data curation, Q.A.; formal analysis, C.W. and Q.A.;
investigation, C.W. and S.N.; methodology, C.W. and Q.A.; supervision, C.W.; validation, C.W.
Funding: This research was funded by the School of Engineering, Korea University, grant number K180911.
Acknowledgments: The author would like to sincerely thank the anonymous reviewers for providing helpful
comments. In addition, thanks are due to the Energies’ staff and editors for handling the paper.
Conflicts of Interest: The authors declare no conflicts of interest.
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