The Effect of Schedules on HVAC Runtime for Nest Learning Thermostat Users - Nest Labs, Inc. September 2013
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WHITE PAPER SUMMARY
The Effect of Schedules on
HVAC Runtime for Nest
Learning Thermostat Users
Nest Labs, Inc.
September 2013
! 11. Introduction
Since the launch of the Nest Learning Thermostat™ in October of 2011, Nest has
undertaken several efforts to quantify the savings potential for customers who install the
thermostat. The first of these efforts was a simulation exercise which modeled typical
household HVAC use with and without the Nest thermostat, the difference being our first
estimate of savings.
As we approach our two year anniversary, we now have a large body of data on actual HVAC
usage from Nest thermostats in the field. This allows us to release this second step
towards the documentation of our energy savings potential, in which we compare actual
with-Nest runtime data against two modeled pre-Nest baselines. As with previous studies
in this field, using models to estimate baseline usage means that the results are heavily
dependent upon the assumptions underlying those models.
The next step in our journey will be to compare actual customer energy data pre and post-
Nest thermostat installation. We anticipate that the runtime reduction claims contained in
this paper may be slightly higher than what we will find in those future studies as this
paper explores a constant hold temperature pre-Nest thermostat baseline, which is
expected to represent only a portion of the overall customer population.
In this study, we estimate how much a Nest thermostat’s schedule reduces HVAC runtime
compared to two different constant hold temperatures. In one model, we use a 72 ˚F hold
temperature baseline, as has been used in other industry studies. In the other model, we
calculate the hold temperature as the 90th percentile of a customer’s Nest thermostat
schedule set points. For both models, we show how much the Nest thermostat’s schedule
- whether learned or set manually - reduces heating runtime in winter and cooling runtime
in summer.
Customers with a Nest thermostat have schedule set points that vary significantly from
hour to hour and day to day. When averaged across all hours of the day, a schedule
effectively reduces the heating set point in the winter and increases the cooling set point in
the summer when compared to a hold temperature baseline. For the 72 ˚F model, these
temperature deltas created by the schedule result in heating and cooling runtime
reductions of 25% and 26%, respectively. For the 90th percentile model, these runtime
reductions are 16% and 20%.
While this study does not purport to document pre-Nest behavior, this level of runtime
reduction is believed to be significantly higher than the runtime reduction achieved with
non-learning thermostats. There is ample qualitative and quantitative documentation of
the shortcomings of programmable thermostats to create granular schedules – mostly
! 2because users resort to set-and-hold behavior or manual adjustments that vary modestly
with changes in weather or other factors. [See Fraunhofer, Meier]
Importantly, this study estimates only the runtime reduction from Nest users’ schedules.
No savings due to HVAC control features – such as Airwave™ or Heat Pump Balance™ – are
included because, although those features lead to substantial efficiency savings, they do
not impact the HVAC schedule. Similarly, this study does not measure the effect of
efficiency-encouraging features such as the Leaf™, Seasonal Savings™, or other features
that encourage a user to adopt a more efficient level of temperature set points .
2. Methods
The savings attributable to schedules implemented on Nest thermostats are based on a
statistical analysis of the relationship between actual temperature set points and heating
and cooling runtime, taking into account actual outdoor temperature and thermostat level
effects. The schedule data used for this study is taken from tens of thousands of Nest
thermostats running during the summer and winter of 2012, distributed across multiple
climate regions. The temperature data is taken from actual average daily temperatures (by
region) during those sample windows. Using this data, we can determine how much less
time (on average) the heating and cooling system worked because of the Nest thermostat
schedule than it would have without the schedule.
For the following paragraphs, heating is used as an example but the methodology is the
same for cooling.
As a baseline for measuring the effect of schedules actually implemented on Nest devices,
we used the 90th percentile high set point for each Nest during the study period. Using the
90th percentile helps account for daily variability in set points. It also creates a more
conservative baseline than the 72 ˚F baseline typically used by industry studies measuring
thermostat efficiency. We calculated this by determining the average set point for each
hour for each Nest during the study period and then calculating the 90th percentile of
these hourly values for each device for all days with heating operation. The use of the 90th
percentile rather than the maximum allows for a little more than two hours each day where
the actual set point could be above what the baseline steady temperature would have
been.
We next created a linear regression model that estimated the total daily runtime. The
specific statistical model fit was a fixed effects regression:
! 3Eit = a + b * ( T_targetit – T_target90thi ) + c * T-outit
with thermostat level fixed effects
Where:
Eit = the daily run time of the heating system for thermostat i on day t
T_targetit = the average target temperature for thermostat i on day t
T_target90thi = The 90th percentile of all hourly set points for thermostat i during days
where Eit is greater than zero
T-outit = the average daily outdoor temperature at thermostat i on day t
Coefficients:
a = model constant = average daily run time when target temperature equals the baseline
and outdoor temperature is 0ºF
b = estimated additional run time from a 1 ˚F delta in target temperature above baseline
c = estimated additional run time from a 1 ˚F increase in outdoor temperature
The inclusion of thermostat-specific fixed effects means that each thermostat has a fixed
average daily run time and all variables in the model are essentially treated as deviations
around their device-level mean values.
After solving from a, b, and c, the avoided heating runtime and the runtime reduction
percentage are calculated from the model output as:
Avoided heating runtime (AHR) = b * Mean( T_targetit – T_target90thi )
Runtime reduction % = AHR / (AHR + Mean( Eit ))
In other words, the runtime reduction is calculated as the percentage of the overall runtime
that would have occurred had it not been for the reduction in set points below the baseline.
! 43. Results
The calculations are conducted on data accumulated between December 1, 2012 through
December 31, 2012 (for the heating data) and August 1, 2012 through August 31, 2012 (for
the cooling data). Only devices with reliable log data were included in the analysis, and
certain data exclusions were implemented for statistical sampling accuracy (e.g., devices
that were cooling during winter, or devices with greater than 90% duty cycle, or devices
outside of the US). These criteria resulted in a sample size of tens of thousands Nest
thermostats. The model was fit for the full dataset and also fit separately for each climate
zone as the coefficient b is expected to vary with climate – taking on smaller values in
colder climates and larger values in milder climates.
For Heating
For heating, the mean target temperature (65.3 ˚F) was 3.9 ˚F below the 90th percentile
temperature (69.2 ˚F) and the mean daily run time was 3.06 hours per day (24hrs), a
12.75%, duty cycle. The regression analysis resulted in calculating the coefficient b at
0.0063, measured as a duty cycle, or an additional .15 hours (about 9 minutes) of runtime
per 1 ˚F delta in temperature above the 90th percentile. A 3.9 ˚F mean temperature
difference from the 90th percentile, therefore, avoids 35 minutes of heating per day. And
the estimated runtime reduction percentage is 16.2% ((0.0063 * 3.9) / (0.1275 + (0.0063 *
3.9))).
Note that the coefficient estimates have very small standard errors, yielding narrow
confidence intervals – the 95% confidence interval on the key coefficient b ranges from .
0075 to .0078.
% of runtime reduction compared to % of runtime reduction compared to
Climate
72 ˚F baseline 90th percentile baseline
Very Cold 25.5% 14.6%
Cold 23.1% 14.3%
Mixed Humid 26.4% 16.8%
Mixed Dry 33.5% 21.8%
Marine 25.4% 17.1%
Hot Dry 25.9% 19.0%
Hot Humid 38.4% 24.7%
All
24.9% 16.2%
(weighted by region)
For Cooling
For cooling season, the mean target temperature (75.9 ˚F) was 2.7 ˚F above the 90th
percentile temperature (73.2 ˚F). The mean daily runtime was 29.2%, measured as a duty
cycle relative to a 24 hour day, or about 7 hours. The regression analysis resulted in
! 5calculating the coefficient b at 0.0267, measured as a duty cycle, or an additional .63 hours
(about 38 minutes) of runtime per 1 ˚F delta in temperature above the 90th percentile. A
2.7 ˚F mean temperature difference from the 90th percentile, therefore, avoids 103
minutes of cooling per day. And the estimated runtime reduction percentage is 19.8%
((0.0267 * 2.7) / (0.292 + (0.0267 * 2.7))).
% of runtime reduction compared to % of runtime reduction compared to
Climate
72 ˚F baseline 90th percentile baseline
Very Cold 13.4% 14.7%
Cold 22.2% 19.8%
Mixed Humid 25.9% 21.8%
Mixed Dry 27.6% 20.1%
Marine 24.2% 18.7%
Hot Dry 28.1% 17.5%
Hot Humid 30.6% 21.3%
All
26.1% 19.8%
(weighted by region)
4. Extrapolating to an Estimated Savings on Heating and Cooling
We have measured clearly the average effect of the schedule variability implemented on
each Nest thermostat device in summer and winter. Because this study does not include
an authoritative pre-Nest thermostat or non-Nest thermostat control group, we cannot
conclusively attribute all the reduced runtime effect of Nest thermostat schedules as
energy savings due to owning a Nest thermostat. Further, because we do not have a
scientifically accurate measure of our users’ behavior before installing a Nest thermostat,
we stop short of concluding that all of the benefit of schedule variability is attributable to
using a Nest thermostat.
However, if we were to assume that a user without a Nest would have a simple set-and-
hold behavior at the 90th percentile of their temperature variability, then all the savings
created by having a schedule could be attributed to a Nest thermostat. This is a
supportable assumption based on the literature documenting the non-use of
programmability in other thermostats [see Fraunhofer, Meier]. The 90th percentile
temperature likely approximates the actual user’s “comfort” temperature – the
temperature used when efficiency and other factors don’t come into play. And the 90th
percentile in our case – 69 ˚F in the winter and 73 ˚F in the summer – are actually more
statistically supportable than a simple 72 ˚F assumption used as the baseline by the EPA
and others attempting to estimate behavior.
! 65. References
[Fraunhofer] O. Sachs, V. Tiefenbeck, C. Duvier, A. Qin, K. Cheney, C. Akers, K. Roth, Field
evaluations of programmable thermostats, Fraunhofer Center for Sustainable Energy
Systems, Cambridge, MA, December 2012
[Meier] A. Meier, C. Aragon, B. Hurwitz, D. Mujumdar, P. Daniel, T. Peffer, M. Pritoni,
How people actually use thermostats, Controls and Information Technology, U.C. Berkeley,
01/01/2010, also see other studies by same author(s)
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