sustech.2022.8671326.docx

2022 IEEEConference on Technologies for Sustainability (SusTech)
Integration of Statistical Models of Residential
HVAC Loads with a Commercial Smart Thermostat
Jeewon Choi, Matthew Robinson, Andrea Mammoli
Department of Mechanical Engineering
University of New Mexico
Albuquerque, New Mexico 87131, USA chatchi923@unm.edu,
mr369@unm.edu, mammoli@unm.edu
Abstract—As part of an effort to develop accurate power flow
simulations in the area of Demand Response (DR) control, we
developed an agent-based model for power consumption by
residential end uses. Electrical power loads occurring in individual
houses are categorized and modeled statistically. We developed a
thermostat model to simulate the HVACpower draw, one ofthe most
important residential loadcategories. In the present work, we replace
the simulatedthermostat fromone of the house models participating
in the aggregatedloadcontrol, with a physical instance of commercial
smart thermostat. Specifically, we selected a ‘Nest Learning
Thermostat’ for integrationinthe loadsimulation. We usedthe Nest
Application Programming Interface (API) for the integration process.
We implemented a PID control system toregulate the temperature of
an environmental chamber where the Nest thermostat is installed.
The environmental chamber is intended to provide the Nest with
conditions similar to what it would experience in a real-life setting.
Learnings fromthe present workwill serve to increase the realismof
large-scale agent-basedsimulations.
Index Terms—Demand response, aggregated load control, smart
thermostat, learningthermostat, Nest API
I. INTRODUCTION
Aggregated residential power uses are considered as prime
resources for controllingloads atthe distribution feeder level.
We recently proposed a simulation framework to develop
scheduling and controls algorithms for aggregated residential
power loads [1].In our framework, residential energy services
are categorized into six groups, namely hot water, space
temperature control, refrigeration, clothes washing / drying,
cooking and lighting. These energy uses are then modeled
from statistical data. The six energy services are modeled for
each individual electric meter that exist within the simulation
framework. Whileaggregated loads areconsistentfromday to
day and relatively predictable,individual meter loads are not.
Here, the necessity of the bottom-up approach to the
characterization of power flow over the feeder arises.Among
the residential loads categories considered, we choose the
HVAC and the water heater as controllable resources. HVAC
provides a sizeable fast response due to its high power draw,
while water heater enables effective peak shifting due to its
high energy storagepotential.The overall objectiveof the load
simulation framework is to design control algorithms that
shape the total distribution feeder load to respond to some
external control signal, such as the power production of a
substation-sited PV array.The purposeof the work presented
here is to verify the realism of the simulation framework,
concerningthe control of HVAC loads.
With the advanceof Internet of Things (IoT) technology, the
availability of smart house appliances is rapidly increasing.
Smart thermostats for regulating space temperature are
becoming popular, enabling the simple utilization of HVAC
load as a controllable resource in aggregated load control. In
this paper, we focus on the integration of a physical smart
thermostat within the load simulation framework introduced
by Mammoli et al. [1] by replacing the model thermostat
switchingalgorithms with the actual responsefrom a physical
thermostat.
We selected the 3rd generation ‘Nest Learning Thermostat’
as the physical smart thermostat to be integrated, that will be
referred to as the Nest thermostat throughout the paper. The
Nest thermostat is attractive in that it possesses advanced
HVAC controlling algorithms that provide user comfort
combined with energy saving.Moreover, the Nest thermostat
is well suited for aggregated control, since it provides an API
that can be used for this purpose.In fact,Nest thermostats are
already providing Demand Rsponse (DR) rewards under
certain programs in collaboration with some energy providers
[2]. The capabilities of the Nest thermostat in this respect will
be considered and evaluated through a set of experiments in
the present work.
II. STATISTICAL LOAD SIMULATOR
We first summarize the statistical load simulation process
discussed in detail previously [1]. A schematic illustration of
the HVAC load simulation is shown in Fig. 1. The number of
load occurrence per day, the load start time and the load
duration for each house are characterized by associated
probability density functions (PDFs). At the beginning of each
day, a schedulefor each load is drawn from PDFs.For the case
of an HVAC event, the schedule generated indicates the
human occupancy of the residential space. For example, on a
particular day,a specific housecould be occupied from 12AM
to 8AM, unoccupied from 8AM to 6PM then occupied from
6PM to 12AM. A temperature setpoint and associated
978-1-5386-7791-9/18/$31.00©2022IEEE

Fig. 1. Statistically drawn house occupancy schedule, setpoint/deadband,
current house model temperature is sent totheloadaggregator. Basedon its
control error, the loadaggregator decides whether to activate or deactivate
the HVAC loads. The simulated thermostat logic responds to thesignalfrom
the load aggregator, and its response is sent to the model. These steps are
repeated in a timeloop.
deadband are set to the value generally desired by user,
although in some cases thesecould be modified for DR control
purposes. In a normal operation situation, when the house is
occupied, a narrower deadband is set to provide comfortable
temperatures to residents.When unoccupied,the deadbandis
set to wider range. DR control can take various forms,such as
a change in the setpoint / deadband or by switchingthe HVAC
operation back and forth. In the previous work [1] the
deadband was simply set wider when unoccupied to reduce
the HVAC load,as a normal smart thermostat would do, while
the state of the HVAC compressor is switched depending on
whether higher or lower load is required by the distribution
system.
During operation, the occupancy, the setpoint / deadband
and the current spacetemperature for each house are sent to
an aggregator. The aggregator calculates the control error,
that is the difference between a desired signal determined by
conditions at the substation and the aggregated HVAC load,
the availablecontrol action,then makes a decision on whether
and how much to collectively activate or deactivate HVAC
loads. A detailed explanation on the aggregated load control
principle is given in the previous work [1]. A signal from the
aggregator is sent to the smart thermostat, which in turn,
based on internal logic, either switches its on / off status or
not. The housespacetemperature at the next time step, based
on the house thermal model in combination with the HVAC
operation, is then calculated.
For this study,we assumethat all houses areequipped with
an HVAC system based on an electrically powered heat pump
for spacetemperature regulation.This setup is widely used in
areas with moderate climate, and will become increasingly
prevalent with increasing electrification of energy services.
The thermal response of an air conditioned space can be
described by [1]:
(1)
,
(2)
Q˙ R = Π × Λ × COP × PAC. (3)
In this model, derived from the physical natureof the system,
Ms is theeffective heat capacity of the conditioned space, Ts(t)
is the temperature of the conditioned space, t is the time, Q˙ L
is the thermal energy exchange between the conditioned
space and its surrounding, Ta is the ambient temperature
outside of the building, Ks models the heat exchange between
the building and the external air, Q˙ R is the rate of energy
outflow or inflow from the space via the HVAC, COP is the
coefficient of performance of the HVAC system and PAC is the
electric power consumed by the compressor. It is assumed
that the heat pump utilizes a fixed-speed compressor,so that
PAC stays constant. Π indicates whether the HVAC mode is
cooling or heating, and Λ is a state function that indicates
whether the compressor is on or off. The HVAC mode is
determined by:
(4)
,
where the HVAC is in cooling mode when Π = 1 or in heating
mode when Π = −1. The switching logic for the simulated
thermostat when the HVAC
is in cooling mode is
described by:
(5)
,
where TL and TU are the lower and upper deadband limits for
the space temperature control. When the temperature is
within the deadband, the state function Λ at a particular time
step remains the same as its previous value,i.e.switchingonly
occurs when the temperature reaches the deadband limits.In
similar manner,when HVAC is in heatingmode:
(6)
.
The HVAC load for an individual houseata particular timestep
is then calculated by
LHVAC = Λ × PAC. (7)

In what follows,we describehow the logic for the HVAC mode
decision and the HVAC state switching of one of the virtual
thermostat in the simulations can be replaced with a physical
instanceof a Nest thermostat.
III. EXPERIMENTAL SETUP
A. Chamber Temperature PID controller
The physical environment in which the Nest thermostat is
immersed mimics the real-time conditions of a house whose
temperature is modeled by (1)-(3). The Nest thermostat yields
Fig. 2. Illustrationof the environmentalchambersimulating a temperature of
the house model. The chamber temperature is controlled by the two fans,
providing thephysicalenvironment for theNest thermostat.
the actual response that we want to utilize within the
multiagent HVAC load simulation.The temperature within the
environmental chamber is controlled using two fan / coil
combinations. The cooling fan pushes air through a
chilledwater heat exchanger and into the chamber. The
heatingfan pushes air through electrically-operated heatcoils.
In the experimental setup shown in Fig. 2, the temperature of
the water running through the heat exchanger and the
temperature of the heatcoils remains steady, therefore the
speed of the fan determines the rate of temperature change
on the chamber. Two fans are connected to the General
PurposeInput Output (GPIO) pins of a Raspberry Pi computer,
that hosts a fan speed control code. Only one of two fans runs
atany one time. The fan speed is calculated based on theerror
between the desired temperature and the current chamber
temperature using a Proportional Integral Derivative (PID)
control method. A PID controller that simply solves (8) is
implemented:
, (8)
where u(t) is a control action, e(t) is the temperature error
and ∆t is a time constant.The PID gains Kp,Ki and Kd are tuned
experimentally so thatthe chamber temperature can precisely
track the house model temperature. The chamber
temperature is the average temperature from a set of T-type
thermocouples placed at strategic locations within the
chamber. An example of the PID controlled response of the
chamber temperature is shown in Fig. 3, in which the physical
chamber temperature tracks the model house temperature.
B. Nest settings
A 3rd generation Nest Learning Thermostat is selected as a
physical thermostat. An electrical circuit of the HVAC system
with a heat pump in an actual residential space,and thewiring
of the Nest thermostat to the HVAC, are shown in Fig. 4. The
24V AC power from the transformer runs through terminals
Fig. 3. With gains of Kp = 0.1, Ki = 0.004, Kd = 0.7, the PID control system
implements thehousemodeltemperatureinsidethe chamber reasonably.
R and C, to power the thermostat. Terminal W1, Y1 and G are
used for sending signals to the heating element relay, the
compressor relay and the fan relay of the heat pump system
respectively. In the present experiment, the Nest thermostat
terminals are not connected to an actual HVAC but the Nest
thermostat still generates HVAC control signals in responseto
its environment. These signals are obtainable through the
voltage measurement in relevant terminals or through the
utilization of the Nest API. The latter method is used in this
work, with details will be covered in the following section. By
detecting connections in its terminals, the Nest thermostat
knows what kind of HVAC system it is controlling. In order to
let the Nest thermostat know that it is associatingwith a heat
pump system, open-ended wires are connected to terminal
W1, Y1 and G as shown in Fig.4.
IV. INTEGRATION OF THE LOAD SIMULATOR AND THE NEST THERMOSTAT
Given this physical environment for the Nest thermostat, it
is now necessary to integrate the response of the Nest
thermostat to the overall simulation. The Nest API is the key
tool utilized in the integration process,and steps taken for this
purpose aredescribed in this section.

A. Nest API
The Nest thermostat supports the API development through
the REpresentational State Transfer (REST) network HyperText
Transfer Protocol (HTTP), which allows a user to read or write
on the Nest thermostat without physically contacting it.
Through REST HTTP, a client(the thermostat itself, or a third-
party agent such as an aggregator) can make requests to
specific server URLs. The data can be manipulated on the
server itself,or transfered back to the client[5]. Nest provides
relevant URLs to API developers. While HTTP requests can be
made via various platforms, in the present work, HTTP
requests have been made in Python through the ‘requests’
library [5].A clientis created in the Nest Developers website
(a)
(b)
Fig. 4. The Nest thermostat wiringfor an actualHVACsystem witha heat pump
and a fan [3], [4]. Its responseis also reachablethroughtheNest API.
that is assigned with a unique client ID, a client secret and an
authorization URL. Before access to private data is available
from the Nest thermostat, an access token [6] must be
obtained. The Nest API uses the OAuth 2.0 protocol for the
authorization process. With the client ID, the client secret ID
and the authorization code, an access token is granted by
making requests. The authorization code is obtained from the
authorization URL with the user consent. Once the access
token has been obtained,itis possibleto makecalls to theNest
API, then access data such as setpoint or HVAC state for the
specific Nestthermostat. Data are pulled in a JavaScriptObject
Notation (JSON) object through the ‘GET’ method of the REST
API, and pushed in through ‘PUT’ method. The process of
authorization is shown in Fig.5.
B. Integration
Having established that data can be pushed in to or pulled
from the Nest thermostat using the Nest API, the integration
of the Nest thermostat to the load simulator can nowproceed.
The sequence of the integration implemented here is:
1) An initial temperature of the house model and the initial
thermostat status are sent to the environmental
chamber and to the Nest thermostat respectively. Then
the initialization is doneaccordingly.
2) The daily occupancy schedule is drawn from the
statistical information in theload simulator.
3) The setpoint / deadband are determined from the user
setting or the DR control insidethe load simulator.The
Fig. 5. Before making requests to thedata, an authorization must bemade.
An access tokenyieldedfrom the authorizationenables requests to theNest
API [6].
setpoint / deadband, the occupancy and the current
house temperature are then sent to the aggregator.
4) The aggregator receives the information provided in
step 3 from a number of simulated house models and
considers its control error, then decides whether to
activateor deactivate HVAC loads.
5) The Nest thermostat receives the final decision fromthe
aggregator, performs internal logic yieldingits response
(HVAC mode / HVAC state) with resulting inside the
chamber accordingly.
6) The Nest thermostat response (HVAC mode / HVAC
state) is sent to the housemodel.
7) Chamber tracks the temperature of the house model at
the next time step through the PID controller.8) Repeat
from step 3 to 7.

A schematic illustration of the process is also shown in Fig. 6.
What is different from the previous work is that the
thermostat logic in the load simulation for one of the house
models is replaced with the physical Nestthermostat.
C. Nest Data
Data types involved in the integration are tabulated in Tab.
I. The HVAC mode and the HVAC state are the ones pulled out
from the Nest thermostat. There are 5 modes for the HVAC
operation. When the HVAC mode is set to ‘Heat’ or ‘Cool’, the
Nest thermostat utilizes a singlesetpoint to control the HVAC
system. It only operates in heating or cooling mode. At the
‘Heat-Cool’ or ‘Eco’ mode, the setpoints for heating and
coolingareset respectively. The HVAC system can either heat
or cool the space. Users may want to set the less comfort
oriented values atthe ‘Eco’ mode. Note that the Nest does
Fig. 6. In the load simulation, the simulated thermostat logic for on of the
house models is replaced with the physical Nest thermostat. The Nest
thermostat receives or sends data through the Nest API. The model
temperature is implemented in a physical chamber via a PID controller
providing theNestthermostatan environmentto yield an actual response.
not support the user set deadband, but supports only the
setpoint. Therefore the setpoint / deadband given from the
load simulator may need to be adjusted accordingly. The Nest
thermostat senses the human occupancy via near-field
activity, far-field activity and ambient light sensor. The Nest
thermostat sets away status of the structure where it is
installed to ‘Home’ when occupied or ‘Away’ otherwise. It can
be set manually as well. However in the present work, we do
not let the thermostat make decision on the occupancy, but
the occupancy will be pushed to the thermostat through the
API. When the away status has turned to ‘Away’ from ‘Home’,
the Nest thermostat automatically switches HVAC mode to the
‘Eco’ regardless of the previous HVAC mode. However, the
‘Eco’ mode is independent of the away status, meaning that
the user can set the HVAC mode to non-‘Eco’ mode when they
are away if it is desired. 3 HVAC states indicate whether the
HVAC system is cooling or heating the space or is off. The
ambient temperature is the space temperature the Nest
thermostat is measuring.
V. RESULTS
The Nest thermostat learns about the thermal response of
the structure/ HVAC combination fromits interaction with the
air conditioned space, and about the schedules from
intractions with its human users. It learns about the HVAC
system it is controllingsuch as how fast the space warms and
cools,and how efficient the system is [7]. The Nest thermostat
used here is trained in real time, by inputing the statistical
occupancy schedule and the setpoint, also by implementing
the house model response in the chamber over several days.
Therefore the Nest thermostat is expected to have learned the
HVAC characteristics of the house model, and these
characteristics are reflected in the Nest thermostat response
shown in this section.
Fig. 7. Behavior ofthe housemodeldriven by thesimulated thermostatlogic
in cooling mode. The HVAC is activated only when the model temperature
goes outsidethe deadband.
A. Simulated thermostat response
The response of the simulated thermostat logic inside the
load simulation is shown in Fig. 7. The simulated thermostat
puts the HVAC into the cooling mode following (4), then it
behaves exactly as demonstrated in (5). The HVAC is
deactivated when the space temperature is lower than the
lower deadband, activated when spacetemperature is higher
than the upper deadband and keeps the previous state within
the deadband.
B. Nest response in various modes
1) Cool Mode: Fig. 8 shows the response of the Nest
thermostat in Cool mode with a setpoint of 24◦ C. In Cool

mode, we observe that the Nest thermostat activates cooling
when the spacetemperature is at the setpoint or above. Also,
the thermostat deactivates cooling a short time after the
temperature goes under the setpoint. Note, for example, that
at around 4000 seconds, the cooling is found to be still
activated whilethe spacetemperature is measured at 23.5 ◦ C,
slightly below the setpoint, but is deactivated shortly
afterward. WhiletheNest thermostat could uselogic based on
an internal deadband, this does not necessarily correspond to
the actual space temperature deadband implemented in our
load simulations. However, a deadband-like behavior is also
observed in the spacetemperature response. The connection
between the internal Nest deadband and the space
temperature deadband is discussed later.
2) Heat-Cool Mode: Figs.9-10 showthe Nest thermostat
response in Heat-Cool mode. Both cooling and heating are
supported in the Heat-Cool mode, with setpoint of 24◦ C and
21◦ C taken at each mode. The Nest thermostat controls the
HVAC the same as in the Cool mode, with the lower internally
set deadband involved. From Fig. 10 at around 9000 seconds,
it is found that the coolingis notactivated when the space

TABLEI
DATA UTILIZED IN THE INTEGRATION
Device Thermostat
Returns/Range Access Note
hvac mode ‘heat’,‘cool’,‘heat-cool’,‘eco’,‘off’ read/write
hvac state ‘heating’,‘cooling’,‘off’ read-only
ambienttemperaturec number read-only
target temperaturec number/9-32 read/write hvac mode=‘heat’ or ‘cool’
target temperature low c hvac mode=‘heat-cool’
number/9-32 read/write
/target temperature high c min gap betweenlow/high=1.5
eco temperaturelowc number/4-21/24-32 read-only hvac mode=‘eco’
/eco temperaturehighc
Structure Returns/Range Access Note
away ‘home’,‘away’ read/write
*Reading or writing the data is availablethrough the Nest API.
Fig. 8. Nest thermostat responsein Cool mode. Cooling is activated when the
space temperature reaches thesetpoint. An internally setlower deadband of
the Nest thermostat is observed tobe 0.5◦ C.
temperature is 24◦ C.Thereforeitappears thatan internally set
upper deadband is used in the Heat-Cool mode. The activation
of the heating is observed in Fig. 9 when the temperature is
below the heating setpoint.
3) Eco Mode: The response of the Nest thermostat in Eco
mode is shown in Fig. 11. In similar manner to the HeatCool
mode, the thermostat uses two setpoints for cooling and
heating.The utilization of thesetpointin thecontrol algorithm,
appears to be almost the same as in the Heat-Cool mode.
However, we can observe that the HVAC is activated with less
strict rules in Eco mode. In the cooling case, the upper
deadband for the cooling setpoint is detected, however it is
found that the lower deadband is not enabled. As soon as the
space temperature reaches the cooling setpoint, the Nest
thermostat turns off the cooling. The same response is
presumed in the heating case. The cooling activation at each
mode found through experiments, is tabulated in Tab. II.
Fig. 9. The Nest thermostat response in Heat-Cool mode. Both cooling and
heating areavailablein this mode. Cooling turns on when thetemperatureis
above the cooling setpoint,heating turns on when thetemperatureis below
the heating setpoint. Thelowerdeadbandis also observed inthis mode.

Fig. 10. The HVAC loadcanbe forcedly increased or decreasedby moving the
setpoint.
Fig. 11. Nest thermostatresponseinEco mode.Cooling is notalways activated
immediately when thespacetemperature is above thecooling setpoint.No
lower deadband set for the cooling setpointin theEco mode. As soon asthe
space temperature comes under the cooling setpoint, the Nest thermostat
turns off the cooling.
TABLEII
COOLING ACTIVATION OF THE NEST THERMOSTAT
Temperature
> Tset
+0.5◦ C
set
Tset
−0.5◦ C
4 4 ×
< Tset
−0.5◦ C
× × ×
* : cooling activated, 4 : cooling sometimes activated or
sometimes not, × : cooling deactivated.
C. Using the Nest thermostat for aggregated DR control
Even though the Nest thermostat does not use the
deadband in the sameway as in thesimulated thermostatlogic
shown in § V-B, we can still utilize it within an aggregated DR
control algorithm,in which the switchingto achievereduction
or increaseof the HVAC load can be activated by adjustingthe
setpoint rather than by actively switchingthe state. When the
Nest thermostat receives a DR signal to reduce the HVAC load,
the setpoint can be adjusted to a less comfort oriented value.
In Fig. 10, the Nest thermostat changing the cooling setpoint
from 24◦ C to 27◦ C, the heating setpoint from 21◦ C to 18◦ C
responding to the DR signal, is shown. We observe that the
state changes as soon as the temperature setpoint is changed,
around 1000 seconds and 6000 seconds, leading to reduction
or increaseof the HVAC load.
Fig. 12. A disagreement between the temperature measured by the Nest
thermostat andthermocouples in the chamberis observed, due to the heat
capacity the Nest thermostatpossesses.
D. Characteristic of the Nest thermostat Response
Before we can usethe Nest thermostat for implementing DR
schemes, it is necessary to fully understand its dynamic
response to temperature changes. Our experimental
apparatus allows such a measurement, since the response of
the thermocouples used in the environmental chamber is
essentially instantaneous, and accurate due to careful
calibration of the thermocouples. Fig. 12 shows temperatures
as indicated by the thermocouples and by the Nest thermostat
insidethe environmental chamber. A discrepancy isobserved.
For example, when the chamber temperature reaches the
upper deadband limit and starts decreasing, the Nest
thermostat continues to increase. Also, the Nest thermostat

measures the temperature with 0.5◦ C resolution, while
thermocouples have better than 0.01◦ C of resolution.
Moreover, sincethe rate of data transfer through the Nest API
is limited, the Nest thermostat temperature and its response
are updated with one minute frequency. While all these
factors contribute to the discrepancy observed, the principal
mechanism responsible for the behavior observed is likely to
be the effective heat capacity embedded in the Nest
thermostat and its temperture measurement apparatus,
whose detailed implementation is not well-documented. We
hypothesize that there is a heat transfer process between the
air surrounding the Nest thermostat and its temperature
measurement system, which can be modeled as a convective
heat transfer to a thermal mass.This behavior can bemodeled
by :
dTn(t)
Mn = Kn[Ts(t) − Tn(t)], (9) dt
where Mn is theeffective heat capacity of the Nest thermostat,
Tn(t) is the modeled Nest Temperature and Kn is a constant
that models the convective heat exchange between the
conditioned space and the Nest thermostat temperature
sensors. Then the control logic that can be used in the
aggregated load simulation, mimicking the control logic
observed in the real
Fig. 13. An actual anda modeledversionoftheNestthermostatcontrol inthe
cooling mode. The Nestthermostat is modeled considering the heat transfer
between the house model (chamber) and the Nest thermostat, the
temperatureresolution and the APIratelimit.
Nest thermostat, can be modeled. The control logic in Cool
mode in particular,could bedescribed by :
,if Tn(t) < Tset −
0.5◦ C
if Tn(t) ≥ Tset (10)
keeps previous state, otherwise,
replacing (5). The response of the Nest thermostat modeled
from (9), considering its temperature resolution and the API
information exchange rate limitations,is shown in Fig. 13. The
suggested model of the Nest shows reasonably equivalent
response to the actual Nest thermostat response, therefore is
expected to bring extra realism to the aggregated load
simulation.
VI. CONCLUSION
The integration of an actual responseof a commercial smart
thermostat, to the aggregated residential load control
simulation framework, developed by Mammoli et al. [1], is
introduced in this paper. With the help of the Nest API, the
Nest thermostat can easily report its status and receive
external signals such as DR / Aggregated load control signals.
As a result, the Nest thermostat is successfully integrated to
the simulation framework, and is able to control the house
model temperature. The ultimate goal of the present work is
to fully understand the interaction between the physical
responseof the Nest thermostat and its environment. The goal
of the present work is achieved,resultingin more ways to add
realism to the simulation. For example, improved model
control logic to beused in the simulation of smartthermostats,
can be implemented based on the new understanding of the
inertial thermal behavior of the Nest thermostat. Such
implementations are expected to bring better realism to the
many-agent load simulations,enablingthe implementation of
control algorithms thatbetter reflect the real world.
ACKNOWLEDGMENT
This research was supported in part by the Mitsubishi
Research Institute, who is entrusted by New Energy and
Industrial Technology Development Organization, under
contractMIRI/EEU 46-001,in partby the U.S. National Science
Foundation,under award 1541148.
REFERENCES
[1] A. Mammoli, M. Robinson, V. Ayon, M. A. Hombrados-Herrera, and M.
Martinez-Ramon, “A simulation framework to develop control and
forecasting tools for aggregated residential energy resources.” preprint
submittedto Energy and Buildings, 2017.
[2] T. Larson, M. Chandra, K. Ward, D. Brannan,andS. Tobias, “Cutting peak
demand - two competing paths and their effectiveness,” in Proc. IEPEC
International Energy Program Evaluation Conference, (Baltimore, MD,
US), Aug. 2017.
[3] Nest, “Nest learning thermostat advanced installation andsetuphelp for
professionalinstallers.”Available: https://nest.com/support/pro/article/
Help-with-installation-and-set-up.
[4] Nest, “Howto installyournest thermostat.” Available: https://nest.com/
support/article/How-to-install-your-Nest-Learning-Thermostat#works/
?mode=guide.
[5] Real Python, “Api integration in python - part1.” Available: https://
realpython.com/api-integration-in-python.

[6] Nest Developers, “Oauth 2.0 authentication and authorization.”
Available: https://developers.nest.com/documentation/cloud/how-to-
auth.
[7] Nest,“Learnaboutearly-onandhowtochangesettings.”Available:https:
//nest.com/support/article/What-is-Early-On.

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