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LETTER
Investigating whether the inclusion of humid heat metrics
improves estimates of AC penetration rates: a case study of
Southern California
McKenna Peplinski1, Peter Kalmus2and Kelly T Sanders1,∗
1Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089, United States of
America
2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, United States of America
∗Author to whom any correspondence should be addressed.
E-mail: ktsanders@usc.edu
Keywords: smart meters, cooling demand, residential AC, humid heat
Supplementary material for this article is available online
Abstract
Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels
intensify the heat stress that populations experience. Although air conditioning (AC) is a key
adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding
of patterns of AC ownership. Developing high resolution estimates of AC ownership is critical for
identifying communities vulnerable to extreme heat and for informing future electricity system
investments as increases in cooling demand will exacerbate strain placed on aging power systems.
In this study, we utilize a segmented linear regression model to identify AC ownership across
Southern California by investigating the relationship between daily household electricity usage and
a variety of humid heat metrics (HHMs) for ~160000 homes. We hypothesize that AC penetration
rate estimates, i.e. the percentage of homes in a defined area that have AC, can be improved by
considering indices that incorporate humidity as well as temperature. We run the model for each
household with each unique heat metric for the years 2015 and 2016 and compare differences in
AC ownership estimates at the census tract level. In total, 81% of the households were identified as
having AC by at least one heat metric while 69% of the homes were determined to have AC with a
consensus across all five of the heat metrics. Regression results also showed that the r2values for
the dry bulb temperature (DBT) (0.39) regression were either comparable to or higher than the r2
values for HHMs (0.15–0.40). Our results suggest that using a combination of heat metrics can
increase confidence in AC penetration rate estimates, but using DBT alone produces similar
estimates to other HHMs, which are often more difficult to access, individually. Future work
should investigate these results in regions with high humidity.
1. Introduction
Global heat stress projections show significant growth
in both exposure to and frequency of dangerous heat
conditions through the 21st century [1,2]. As tem-
peratures and humidity rise, widespread access to
air conditioning (AC) will be crucial to mitigate the
health risks posed by exposure to extreme heat events
[3–5]. However, growth in AC adoption and use has
major implications for the world’s energy systems
and, depending on the pace of decarbonization effort,
greenhouse gas emissions. By 2050, it is estimated that
AC will be the second largest source of global electri-
city demand due in large part to the huge growth in
cooling units expected in developing countries, many
of which are in the hottest regions of the world [6].
Increasing cooling demand will exacerbate the intens-
ity and frequency of peak demand events putting even
more strain on aging electricity systems [7,8]; when
these electricity systems fail, power outages interrupt
vital services and increase heat exposure, putting pub-
lic health at risk [9–13].
© 2023 The Author(s). Published by IOP Publishing Ltd
Environ. Res. Lett. 18 (2023) 104054 M Peplinski et al
Although the relationship between electricity
demand and temperature has been well established
[14–16], there are aspects of the thermal environ-
ment beyond air temperature that influence human
comfort levels [1,10,17,18], and therefore, energy-
consuming behaviors. Heat stress, a physiological
response to extreme humid heat conditions that limit
the body’s ability to regulate temperature, depends
on a combination of both temperature and humid-
ity, among other factors [1,10,19–23]. Despite stud-
ies showing cooling demand is impacted by humid-
ity conditions as well as temperature [24–27], most
research to date uses temperature-derived variables
as the only climate indicators in predicting elec-
tricity demand [7,28–31]. Because of the proven
link between humid heat and energy demand, it is
likely that humidity levels impact both AC ownership
and patterns of adoption. However, the connection
between AC ownership and humidity has not been
explored in the literature.
As the power sector transitions to a grid that more
heavily relies on variable sources of generation and
demand side management, grid planners will bene-
fit from more accurate estimates of AC ownership at
local scales to manage future cooling loads [32] and
potentially leverage these loads for demand side man-
agement strategies [33,34]. Developing a thorough
understanding of spatial and temporal trends in cool-
ing behavior would also help identify areas with high
growth potential for AC adoption, as well as com-
munities that lack access to AC and are most vul-
nerable to extreme heat. Developing estimates and
projections of residential AC ownership is difficult
because detailed information on homeowner appli-
ances and energy behavior is rarely publicly available.
Most reported AC penetration rates are from appli-
ance saturation surveys or residential energy con-
sumption surveys that are carried out by federal or
state governments [35–37]. These studies are time
intensive, expensive, and generally limited in spa-
tial scale to larger geographic regions (e.g. climate
zones or groups of states). Some studies have used
the survey data to build predictive models of AC
ownership [38–42]. For example, researchers used
responses from the American Housing Survey and
American Community Survey to estimate the prob-
ability of AC ownership in census tracts across 115 US
metropolitan areas and found patterns of inequality
in AC access [42]. However, the empirical model con-
structed in the study is based on nationwide trends
that might not hold true in certain regions; spe-
cifically, the model did not perform as well in rel-
atively cool climates (e.g. the Northeast, Northwest,
Midwest, Colorado, and coastal California). The
coarse resolution of survey data limits the ability of
these models to develop highly resolved estimates of
AC ownership.
As smart meters have become increasingly com-
mon, their electricity data records have been used
in a variety of energy building studies to investigate
historical energy behavior and demand with much
higher level of detail than previously possible
[43–46]. Chen et al developed a methodology to
determine whether or not a home has AC using
household level smart meter electricity records and
local weather data, and then characterized AC penet-
ration rates at the census tract level across Southern
California [47,48]. This study was novel in generat-
ing highly resolved estimates of AC ownership across
a large geographic region with widely varying micro-
climates, building stock, and socioeconomics. The
methodology was also used to identify populations
that might be especially vulnerable to extreme heat
events due to the confluence of low rates of AC pen-
etration and high poverty levels [49]. These studies
resulted in highly resolved estimates of AC ownership
across a large geographical area but were limited by
their focus on dry bulb temperature (DBT) alone to
characterize climate–energy interactions.
More recently, studies that model electricity
demand have included humidity-related indices and
found that humidity is a critical element in estimat-
ing both cooling and overall demand [24,31,50–53].
In one study, models were developed using monthly,
state-level electricity from the United States and vari-
ous climate indicators to project residential electri-
city demand under climate change scenarios. The res-
ults showed that projections based solely on DBT
can underestimate electricity demand by as much as
10%–15% [24]. A second study used electric load
data from EIA and hourly meteorological data for
four electricity regions of southeastern United States,
and found that apparent temperature (AT), which
captures both humidity and temperature, was bet-
ter for modeling historical electricity demand than
DBT alone [52]. The projected demand using AT
was also higher for all four regions than when using
DBT. These studies are significant because they show
that humid conditions will alter electricity demand
for space cooling, but they focus only on growth
in demand from current units, ignoring potential
installations of new AC.
While previous studies have assessed the demand
for cooling using DBT, to our knowledge, no study
has used humidity-related indices to identify pat-
terns of AC ownership. We believe this relation-
ship warrants investigation, as the literature has
shown that humidity impacts both human percep-
tion of heat and overall demand for cooling. In this
study, we compute a variety of humid heat metrics
(HHMs) from local weather station data that encom-
pass both temperature and humidity and build on
the methodology developed by Chen et al to test our
hypothesis that estimates of AC penetration rates,
i.e. the percentage of homes in a defined area that
have AC, can be improved by considering humid-
ity as well as temperature. Southern California is a
particularly interesting case study to develop high
2
Environ. Res. Lett. 18 (2023) 104054 M Peplinski et al
resolution estimates of AC penetration rates because
the building stock, socioeconomics, and microcli-
mates, which greatly impact the likelihood of a house-
hold having AC, all vary significantly across relat-
ively small spatial extents [54]. Further, in California,
75% of people have AC, which is roughly 16 points
lower than the national average [55]. Therefore, it
is especially prudent to uncover patterns and trends
in AC ownership in California to foresee where
growth in electricity demand might occur and loc-
ate communities that are most at risk during extreme
heat events.
2. Methods
2.1. Electricity records
Southern California Edison (SCE), an investor-
owned utility, provided hourly residential electri-
city data from the years 2015 and 2016 for roughly
200 000 households (including single family homes
and apartment units within multifamily buildings)
within their service area. The customers were ran-
domly selected so that the dataset is statistically rep-
resentative of Greater Los Angeles’s 4.5 million resid-
ential households at 99% confidence level. SCE also
supplied the street level address of each customer,
which allowed for a highly detailed geospatial ana-
lysis. All electricity data were stored on USC’s cen-
ter for High-Performance Computing with a highly
secure HPC Secure Data Account, to remain in line
with the security and confidentiality requirements of
SCE.
Steps were taken to screen outliers in the data that
might distort the relationship between household
electricity and the study’s heat metrics. Households
with less than half a year of electricity records were
removed from the dataset, as well as homes that had
less than 20 kWh of annual electricity demand, the
amount of electricity an average home in California
consumes each day [56]. We omit these homes as
including unoccupied homes could distort estim-
ations of AC penetration rates Homes with solar
panels were removed from the dataset because elec-
tricity demand met by solar panel generation is
not measured by the smart meters. Thus, the gap
between measured and actual demand would con-
volute the relationship between the home’s electri-
city consumption and outdoor weather conditions.
The data provided by SCE does not identify custom-
ers with residential solar, so a method developed by
Chen et al was employed to detect these homes based
on their hourly electricity consumption [48]. Only a
small fraction of the homes were identified as having
solar panels (1%–2%) so their omission should intro-
duce no significant bias during the period studied. As
solar penetration increases over time, this assumption
would need to be reevaluated in future studies. After
the screening steps, 158 114 households remained in
the dataset.
2.2. Weather data and heat metrics
Local weather data were collected at an hourly
resolution for the years 2015–2016 from three dif-
ferent sources of land-based weather stations: the
California Irrigation Management Information
System (CIMIS), the National Oceanic and
Atmospheric Administration’s National Center
for Environmental Information (NCEI), and the
Environmental Protection Agency Air Quality System
(EPA AQS) [57–59]. In total data from 102 stations
were used. Each of the sources contain data from
land-based weather stations across the Southern
California region that are automated and quality
controlled. Hourly ambient DBT, relative humid-
ity (RH), and wind speed were measured by all
three sources.
Dew point (DP) temperature was also available
from CIMIS and NCEI stations, and NCEI stations
measured wet bulb temperature (WBT). Using the
DBT and RH, DP and WBT were calculated for the
stations that did not record their values. Effective
temperature (ET), AT, and Steadman’s model of heat
index (HI) were computed using the weather data
retrieved from the weather stations described above.
These HHMs were selected because they are com-
monly discussed in literature regarding human per-
ception of heat and heat related public health risk
and incorporate humidity in their calculations. There
is also stronger consensus within the heat literat-
ure on their definition and how to calculate them,
while many other heat metrics are not as well defined.
Table 1defines both the measured and calculated heat
metrics used in this study. The formulas and packages
used to compute the calculated metrics are outlined in
the SI.
2.3. Statistical model
The segmented linear regression developed by Chen
et al is implemented in this study to model the rela-
tionship between residential electricity use and each
of the heat metrics [47]. In that model, a household’s
daily aggregated electricity demand was regressed
against daily average DBT to determine whether the
household had AC during the period of study. To
test which heat metric best estimates AC ownership,
we therefore aggregate hourly electricity demand to
daily electricity demand for each of the households
and regress against the daily average value across each
respective heat metric. Figure 1shows the segmented
linear regressions between daily aggregated electricity
use and each of the six heat metrics for an example
household in the study region.
A distance cutoff was implemented so that any
household more than 20 miles away from a weather
station was removed from the analysis (refer to SI
S1 for distribution of distance from household to
weather station). This distance was selected to try to
keep as many homes as possible in the dataset without
matching homes to weather stations that would not
3
Environ. Res. Lett. 18 (2023) 104054 M Peplinski et al
Table 1. Description of heat metrics used in this study.
Metric Definition
Dry bulb temperature The ambient temperature measured by a thermometer, referred to as air temperature
[60].
Wet bulb temperature The temperature of adiabatic saturation measured by a thermometer covered with a wet
cloth. At 100% relative humiditya, the wet-bulb temperature is equal to the air
temperature. At lower humidity, the wet-bulb temperature is lower than dry-bulb temp
[60].
Dew point temperature The temperature that air needs to be cooled to achieve 100% relative humiditya. The
higher the relative humidity, the closer the dew point to the actual air temperature [60].
Heat index Human perceived equivalent temperature when considering air temperature and relative
humiditya[60].
Apparent temperature Temperature equivalent perceived by humans (feels like) caused by combined effects of
air temperature, relative humiditya, and wind speed [61]. According to the National
Digital Forecast Database, the apparent temperature is equal to the dry bulb temperature
between 50 and 80 ◦
F, the heat index above 80 ◦
F, and the wind chill below 50 ◦
F [62].
Effective temperature The temperature of saturated air that would incur the same level of discomfort for
humans as the measured dry bulb temperature and relative humiditya. Thus, the
equation for effective temperature includes terms for both the dry bulb temperature and
relative humidity [63].
aRelative humidity: the amount of water vapor present in air expressed as a percentage of the amount needed for saturation at the same
temperature [60].
Figure 1. An example set of segmented linear regressions for one home in La Crescenta, CA that was identified as having AC with
all six heat metrics evaluated on each x-axis.
accurately represent the local conditions of the home.
On days where weather station data were missing,
households were matched with weather data from the
next closest weather station (so long as station was
within 20 miles from home).
The segmented linear regression depicts two key
pieces of information. The first is the stationary point
temperature (SPT) which is the inflection point on
the plot and is regarded as the outdoor temperature at
which a household is expected to turn on their AC if
they have it in their home. The second takeaway is the
electricity–temperature sensitivity (E–T sensitivity),
the slope of the line to the right of the SPT. The slope
is the sensitivity of a household’s electricity consump-
tion to the ambient temperature and is impacted by
occupant and household characteristics that are not
explicitly explored in this study due to data limita-
tions (e.g. cooling preferences, occupancy rates, insu-
lation, AC efficiency). In this study, multiple meas-
ures of heat are used, and the temperature refers to
the heat metric used in a given regression (e.g. WBT,
ET). The r2values, which measure the goodness of fit
of the segmented linear regression model, are recor-
ded for the values to the right of the SPT to compare
the correlations between electricity and temperature
across the heat metrics.
4
Environ. Res. Lett. 18 (2023) 104054 M Peplinski et al
A household is determined to have ACif two con-
ditions in the segmented linear regression are met.
The first condition is that the slope to the right of
the SPT (referred to as slope-right) is greater than
zero, because it is presumed that a household with AC
would have electricity consumption that positively
correlates with increasing ambient temperatures. The
second condition is that the absolute value of the
slope-right is greater than the absolute value of the
slope to the left of SPT (referred to as slope-left).
A majority of homes in California are heated with
natural gas, meaning the slope-left should typically
be near-zero for these homes [64]. Thus, a household
with an absolute slope-right value smaller than the
absolute slope-left value likely does not have AC, as
the household’s electricity demand at temperatures
above the SPT is only nominally dependent on the
temperature. This condition is set to rule out homes
that have near-zero E–T sensitivities caused by noise
or slightly higher electricity consumption of appli-
ances on warmer days. If a household does not meet
these criteria, it is assumed that the household did not
use an AC during the period of study. Examples of
households that do and do not meet these criteria are
shown in the SI.
The segmented linear regression is run for each of
the households in the study, across each of the heat
metrics defined in table 1. After running the regres-
sion for each individual household, an AC penetra-
tion rate is computed for each census tract by dividing
the number of homes identified as having AC within
a census tract by the total number of homes available
in our dataset in that census tract. Differences in the
computed AC penetration rates, E–T sensitivity, and
SPT when separate heat metrics are used are discussed
below.
2.4. Spatial analysis
Maps were created to illustrate the geospatial vari-
ations in AC ownership across the study region and
differences in estimated AC penetration rate for each
of the heat metrics used. The results of the household
regression for each of the six heat metrics were aggreg-
ated to the census tract level to protect the privacy of
the customer data. Then, estimates of AC penetration
rates were depicted using choropleth maps and census
tract boundary shapefiles from the US Census Bureau
[65]. The climate zones as defined by the California
Energy Commission were also depicted to generate
a better understanding of how AC ownership differs
across the microclimates of the region [66].
3. Results and discussion
3.1. Differences in estimated AC penetration rates
The AC penetration rates from each HHM were com-
pared against the AC penetration rates found using
DBT. Areas shown in red have lower rates of AC pen-
etration (when the given heat metric is used instead
of DBT) and tend to be in inland and desert areas,
which are hotter and drier; areas shown in blue have
higher estimates and are typically coastal, which tend
to be cooler and more humid. The estimates for AT,
ET, and HI closely align with DBT, while more signi-
ficant differences are observed in the maps for WBT
and DP.
A summary of the study region’s average regres-
sion results for each heat metric is shown in table 2.
The estimated AC penetration rate ranges from 73%
(DBT) to 83% (DP). In general, there is agreement
between the AC penetration rates estimated by the
HHMs and DBT. However, the regional estimates of
AC penetration rates with AT, ET, and HI are closer
to the estimates produced with DBT than WBT or
DP are, a trend also depicted in the choropleth maps
in figure 2. The regional average E–T sensitivity val-
ues computed by the regression models range from
0.08 kW ◦
C−1for DP to 0.15 kW ◦
C−1for ET across
the six heat metrics evaluated (see table 2).
Regional average r2values for each heat metric are
also given in table 2. The model is fit to minimize the
r2value for all data points, but the reported r2val-
ues only consider the set of data points to the right
of the SPT in the segmented regression model, as we
are most interested in how a home responds to tem-
perature at the critical point at which a cooling sys-
tem is turned on. The r2values for the six heat met-
rics range from 0.15 to 0.40. DP represents the lower
boundary of this range, and HI and AT both have an
r2of 0.40. In general, these results show that heat met-
rics that include humidity either have an r2value that
is lower or similar to the regression model analyzing
DBT alone.
These results contradicted our initial hypothesis
was that HHMs would be significantly better suited
for identifying whether a home has AC. We expected
that household cooling demand would be best cor-
related with heat indices that account for humidity,
based on the understanding that a person’s comfort
level is impacted by both temperature and humid-
ity. The weak correlation between WBT and demand
could be explained by the findings in Vecellio et al [67]
which show that WBT does not appropriately cap-
ture nonlinear function of temperature and humidity
that is appropriately matched to human physiology
[67]. Additionally, the other three HHMs performed
no better than DBT. The results make sense from
an engineering perspective, given that the regional
climate zones analyzed in this study generally do
not consistently experience high humidity. In an AC
unit, the temperature and moisture content of out-
door air is reduced air upon interaction with the
AC’s cooling coils, which are kept below the air’s
DP [68]. While there is an energy penalty associated
with dehumidifying the air (i.e. the latent load), the
total energy load is dominated by the sensible load
(i.e. the energy required to reduce the air temperat-
ure) except in extremely humid climates [69]. Hence,
5
Environ. Res. Lett. 18 (2023) 104054 M Peplinski et al
Table 2. Summary of the study region’s averaged regression results for each heat metric.
Metric
AC penetration
rate (%) SPT (◦
C)
ET sensitivity
(kW ◦
C−1)r2
Dry bulb temperature 73 19.4 0.10 0.39
Wet bulb temperature 80 14.2 0.13 0.28
Dew point temperature 83 10.6 0.08 0.15
Heat index 75 19.1 0.10 0.40
Apparent temperature 74 19.4 0.11 0.40
Effective temperature 77 17.9 0.15 0.39
Figure 2. Choropleth maps depicting the difference between census tract level AC penetration rates estimated with each HHM
and DBT. The difference is found by subtracting the AC penetration rates computed with DBT from the AC penetration rates
computed using the each of the HHMs (a) WBT, (b) AT, (c) ET, (d) HI, and (e) DP. Generally, the AC penetration rate computed
with a HHM is lower (red) in desert regions and higher (blue) in coastal regions than when DBT is used.
it is likely that the low humidity levels in Southern
California do not cause an observable signal in the
overall electricity demand of a household (see SI for
distribution of RH and DBT across study region).
Consequently, our results may be region specific; a
city that is both hot and humid likely demonstrates
a stronger link between humidity metrics and over-
all demand. However, people living in more humid
climates are also more likely to be more tolerant
of higher humidity levels than those living in dryer
regions due to regional acclimatization [70], which
might dilute an observable relationship between cool-
ing load and humidity. Conducting studies in regions
with diverse climates would provide insight into the
interactions between humid heat, human behavior
and acclimatization, and electricity demand, but the
lack of availability of household level electricity data
is a limiting factor.
While the difference in r2results from the regres-
sion models are not definitive enough to state which
of the metrics should be used to determine AC owner-
ship, evaluating AC penetration with multiple metrics
can provide higher confidence in the estimations. In
figure 3, the homes were grouped by the number of
heat metrics that identified the household as having
AC, and the breakdown of which heat metrics identi-
fied the households as having AC within each group-
ing is shown in the bar chart. In figure 3(a), 69% of
households were determined to have AC using the
segmented linear regression methodology with all five
of the heat metrics (note that DP is excluded because
preliminary results showed it was a poor predictor of
AC ownership). Figure 3offers insight into our con-
fidence in the total AC penetration rate across the
region of study, which is highest for the set of homes
identified as having AC based on agreement between
5 metrics (69%) and slightly less as we add the addi-
tional homes identified with at least 4 metrics (+3%
of homes) or 3 metrics (+2%), raising the overall AC
penetration rate estimates to 72% and 74%, respect-
ively. We have low confidence for regional AC penet-
ration rate estimates in the range of 76%–81%, which
includes all homes identified with at least one metric.
These results align with regional estimates (table
of estimates given in SI) conducted by [36,42,71–
74], suggesting that we can have high confidence in
the 69% of homes that were identified as having
AC by all heat metrics. Although the most recent
6
Environ. Res. Lett. 18 (2023) 104054 M Peplinski et al
Figure 3. (a): Percentage of homes identified as having an AC with all five heat metrics (i.e. consensus across all metrics).
(b)–(e): The additional homes identified as having AC with a consensus of nmetrics. (f): Summary of the percentage of homes
identified as having AC by nheat metrics. The transition from dark to light blue implies diminishing confidence in the homes
identified as having AC (e.g. we have more confidence in the homes identified with 5 metrics, represented with dark blue, than the
homes identified with 1 metric, represented with light blue).
California Residential Appliance Saturation Survey
estimates that 86% of customers in SCE territory have
AC, the estimate is based on 2019 survey data rather
than 2015 and 2016. Additionally, the survey includes
any household that reported owning an AC, regard-
less of how often they use it, and our method might
not capture households that use their AC infrequently
(e.g. a vacation home with low average occupancy
throughout the year). Similarly, the study by Romitti
et al [42] reports a higher average AC penetration rate,
81%, for the Los Angeles–Long Beach–Anaheim area
but also uses more recent survey data and would cap-
ture all AC ownership, regardless of use.
4. Conclusion
Highly resolved estimates of AC ownership are essen-
tial to prepare for future cooling demand and identify
communities who will be most at risk to future
extreme heat events. However, determining AC pen-
etration rates at fine scales is difficult due to the lack
of availability of household level data and limited
understanding of how AC use behavior responds
to varying heat metrics. This study improved upon
existing methods of predicting AC penetration rates
by incorporating a variety of humidity and temper-
ature related heat metrics with a robust dataset of
electricity records for ∼160 000 homes in Southern
California.
In total, 81% of the households were identified as
having AC by at least one heat metric (when exclud-
ing DP), while 69% of the homes were determined
to have AC with a consensus across all five of the
heat metrics. These results are aligned with the res-
ults from other studies of the region (SI S4). A limit-
ing factor of any method used to estimate AC pen-
etrations rate is that there is no ground truth of
residential AC ownership to validate against, partic-
ularly across small spatial extents, which is import-
ant for understanding heat vulnerability across dif-
ferent socio-economic groups. Hence, our method is
advantageous because it provides insight into our rel-
ative certainty in estimating if a home uses AC based
on five analyses of electricity usage and a respective
heat metric. Accordingly, while this analysis suggests
that between 69% and 81% of households in SCE have
AC, we have higher confidence that the true range is
69%–74% of homes for the years analyzed.
7
Environ. Res. Lett. 18 (2023) 104054 M Peplinski et al
The computed regional AC penetration rates
range from 73% for DBT to 83% for DP. Maps
of AC penetration rates show that there are geo-
spatial variations in the prediction of AC owner-
ship. For DP and WBT, where regional estimates
diverged from DPT more significantly, the dryer, hot-
ter regions were estimated to have lower AC owner-
ship than when DPT was used. The opposite was true
in the milder, more humid coastal regions (i.e. cal-
culated AC penetration rate was higher for DP and
WBT than DBT). The regional average r2values vary
from 0.15 to 0.40, and the highest values are from
HI and AT. WBT performed worse than DBT (0.28
vs 0.39), suggesting that the demand for cooling is
more dependent on air temperature than humidity.
While this contradicts our initial hypothesis, it makes
sense with thermodynamic principles, and results
might be different in areas of very extreme humid-
ity where the latent load of AC units is much more
pronounced.
While it is difficult to draw a conclusion as to
which heat metric is most accurately predicts AC
ownership from the results of this study, using DBT
alone possesses several advantages and performed
similarly to or better than other metrics within this
study region. DBT is a well understand metric of heat,
and DBT data can be easily retrieved from a vari-
ety of historical weather sources, unlike other heat
metrics. Additionally, regional meteorological mod-
els and climate models can predict DBT with more
accuracy than humidity and heat metrics that include
humidity [75–78]. We chose Southern California as
our study region because it is one of the only regions
where researchers can gain access to smart meter data
at a large scale (through a formal process outlined by
California’s Public Utilities Commission) [79] across
diverse climate zones, and it is expected to have rel-
atively large increases in AC adoption in the com-
ing years when compared to other regions of the
United States that already have high AC penetration
rates. While we acknowledge that the outcome of
this study may be regionally specific, the outlined
methodology can serve as a framework that should
be repeated in more humid climates as smart meter
data becomes available to confirm this conclusion.
Furthermore, repeating this study with higher resolu-
tion temperature and heat metrics would be desirable
to ensure that the distance to weather station, which
can be as much as 20 miles in this analysis, does not
skew results.
Data availability statements
The data that support the findings of this study may
be available from the corresponding author upon
reasonable request. These include processed data used
to create figures. The raw data are not publicly avail-
able for legal and/or ethical reasons.
Acknowledgments
This work was funded in part by the National
Science Foundation under grants CBET-CAREER
1845931 and CBET-CAREER 1752522, as well as the
JPL (Sponsor: NASA) Neighborhood-Scale Extreme
Humid Heat Health Impacts Grant. Computation for
the work described in this paper was supported by
the University of Southern California’s Center for
Advanced Research Computing (carc.usc.edu). We
also thank Southern California Edison for access to
the smart meter data.
Funding
The funding sources that supported this work
include:
•CBET-CAREER 1845931
•CBET-CAREER 1752522
•JPL (Sponsor: NASA) Neighborhood-Scale
Extreme Humid Heat Health Impacts Grant
ORCID iDs
McKenna Peplinski https://orcid.org/0000-0002-
6353-6211
Peter Kalmus https://orcid.org/0000-0001-7557-
8614
Kelly T Sanders https://orcid.org/0000-0003-
4466-0054
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