U.S. patent application number 14/575095 was filed with the patent office on 2015-06-18 for systems and methods for signature-based thermostat control.
The applicant listed for this patent is Google Inc.. Invention is credited to Yoky Matsuoka, Michael Plitkins, David Sloo, Mark D. Stefanski.
Application Number | 20150168003 14/575095 |
Document ID | / |
Family ID | 53367960 |
Filed Date | 2015-06-18 |
United States Patent
Application |
20150168003 |
Kind Code |
A1 |
Stefanski; Mark D. ; et
al. |
June 18, 2015 |
SYSTEMS AND METHODS FOR SIGNATURE-BASED THERMOSTAT CONTROL
Abstract
A system includes a thermostat that controls a heating,
ventilation, and cooling (HVAC) system of a structure in accordance
with a signature-based temperature program. The thermostat includes
one or more sensors configured to collect occupant activity data, a
network interface configured to communicate with at least one
online resource, a memory configured to store a signature-based
temperature model, and a processor. The processor is configured to
determine a temperature to implement from an output of the
signature-based temperature model, wherein a current value of the
at least one model input and a current measure of occupant activity
are provided as inputs to the signature-based temperature model.
The processor is further configured to provide control signals to
the HVAC system to implement the determined temperature.
Inventors: |
Stefanski; Mark D.; (Palo
Alto, CA) ; Plitkins; Michael; (Berkeley, CA)
; Sloo; David; (Menlo Park, CA) ; Matsuoka;
Yoky; (Palo Alto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Google Inc. |
Mountain View |
CA |
US |
|
|
Family ID: |
53367960 |
Appl. No.: |
14/575095 |
Filed: |
December 18, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61917533 |
Dec 18, 2013 |
|
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Current U.S.
Class: |
165/237 ;
165/268 |
Current CPC
Class: |
F24F 2130/10 20180101;
F24F 11/58 20180101; F24F 11/70 20180101; F24F 2120/14 20180101;
F24F 2120/10 20180101; F24F 2140/60 20180101; F24F 11/52 20180101;
F24F 11/30 20180101; F24F 2130/20 20180101; F24F 11/46 20180101;
F24F 2130/00 20180101; F24F 2110/12 20180101; F24F 11/64
20180101 |
International
Class: |
F24F 11/00 20060101
F24F011/00 |
Claims
1. A system, comprising: a thermostat that controls a heating,
ventilation, and cooling (HVAC) system of a structure in accordance
with a signature-based temperature program, wherein the thermostat
comprises: one or more sensors configured to collect occupant
activity data; a network interface configured to communicate with
at least one online resource; a memory configured to store a
signature-based temperature model; a processor configured to:
determine, via the network interface, a current value of at least
one model input from at least one online resource; determine, via
the one or more sensors, a current measure of occupant activity
from the occupant activity data; determine a temperature to
implement from an output of the signature-based temperature model,
wherein the current value of the at least one model input and the
current measure of occupant activity are provided as inputs to the
signature-based temperature model; and provide control signals to
the HVAC system to implement the determined temperature.
2. The system of claim 1, wherein the one or more sensors comprise
thermal sensors, motion sensors, light sensors, electromagnetic
sensors, sound sensors, vibration sensors, gas sensors, or
combinations thereof.
3. The system of claim 1, wherein the at least one online resource
comprises a thermostat service, a weather website or datafeed, an
emergency website or datafeed, an utility website or datafeed, a
stock exchange website or datafeed, or a combination thereof.
4. The system of claim 1, wherein the network interface is
configured to receive the signature-based temperature model from an
online thermostat service and store the signature-based temperature
model in the memory of the thermostat.
5. The system of claim 4, wherein the processor is configured to
select the signature-based temperature model from a plurality of
signature-based temperature models hosted by the online thermostat
service based on classification data stored in the memory of the
thermostat.
6. The system of claim 5 wherein the classification data comprises
information regarding the structure, the thermostat, the HVAC
system, one or more occupants of the structure, or a combination
thereof.
7. The system of claim 1, wherein the at least one model input
comprises weather data, emergency data, closure notification data,
utility data, economic data, traffic data, transportation data, or
a combination thereof.
8. The system of claim 1, wherein the occupant activity data
comprises a number of occupants in the structure, a location of the
occupants in the structure, an air quality and composition
measurements in the structure, an activity level of the occupants
of the structure, a level of network traffic on a network of the
structure, or a combination thereof.
9. The system of claim 1, wherein the signature-based temperature
model is constructed, at least in part, by correlating in time
historical temperature preferences from one or more temperature
setpoint schedules with historical values of the at least one model
input, historical measures of occupant activity, or a combination
thereof.
10. The system of claim 1, wherein the thermostat is configured to
not control the HVAC system according to a time-based temperature
program.
11. The system of claim 1, wherein current time is not one of the
at least one model inputs provided as inputs to the signature-based
temperature model to determine the temperature to implement.
12. The system of claim 1, wherein the system comprises one or more
additional sensors that are not disposed in a housing of the
thermostat but are communicatively coupled to the processor of the
thermostat, wherein the one or more additional sensors are also
configured to collect occupant activity data.
13. A method, comprising: receiving thermostat data collected by
one or more thermostats respectively controlling a heating,
ventilation, and cooling (HVAC) system of one or more structures,
wherein the thermostat data comprises plurality of temperature
setpoints, and wherein each temperature setpoint is associated with
a time and a temperature; for each temperature setpoint of the
plurality of temperature setpoints: determining historical values
for one or more model inputs, wherein the historical values occur
at or near the time associated with the temperature setpoint, and
determining correlations between the historical values of the one
or more model inputs and the temperature associated with the
temperature setpoint; and constructing at least one signature-based
temperature model based, at least in part, on the correlations.
14. The method of claim 13, wherein the thermostat data comprises
occupant activity data, and wherein the occupant activity data
comprises a number of occupants in the one or more structures, a
location of occupants in the one or more structures, air quality
and composition measurements of the one or more structures, an
activity level of the occupants in the one or more structures, a
level of network traffic on a network of the one or more
structures, or a combination thereof.
15. The method of claim 14, comprising: for each temperature
setpoint of the plurality of temperature setpoints: determining a
piece of historical occupant activity data collected at or near a
time associated with the temperature setpoint, and determining
additional correlations between the piece of historical occupant
data and the temperature associated with the temperature setpoint;
and constructing the at least one signature-based temperature model
based, at least in part, on the additional correlations.
16. The method of claim 13, wherein the one or more model inputs
comprise weather data, and wherein the weather data comprises
humidity data, outdoor temperature data, seasonal data, air quality
data, sunlight level data, precipitation data, or a combination
thereof.
17. The method of claim 13, wherein the one or more model inputs
comprise emergency data, and wherein the emergency data comprises
volcanic activity warnings, wild fire warnings, blizzard warnings,
hurricane warnings, tornado warnings, flash flood warnings,
earthquake warnings, or a combination thereof.
18. The method of claim 13, wherein the one or more model inputs
comprise closure notification data, and wherein the closure
notification data comprises closure notifications for schools,
offices, roads, bridges, cities, or a combination thereof.
19. The method of claim 13, wherein the one or more model inputs
comprise utility data, and wherein the utility data comprises power
rates, capacity of a utility network, load of the utility network,
peak power rates, peak power times, or a combination thereof.
20. The method of claim 13, wherein the one or more model inputs
comprise economic data, and wherein the economic data comprises a
price of a particular stock or commodity, a performance measure of
a market or an exchange, a consumer confidence index, a consumer
price index, a volatility index, an unemployment rate, or a
combination thereof.
21. The method of claim 13, wherein the one or more model inputs
comprise traffic data, and wherein the traffic data comprises
locations and durations of traffic jams, traffic accidents, road
construction, detours, road hazards, or a combination thereof.
22. The method of claim 13, wherein the one or more model inputs
comprise transportation data, and wherein the transportation data
comprises public transportation schedules and routes, airport
schedules, or a combination thereof.
23. The method of claim 13, wherein the thermostat data comprises
classification data.
24. The method of claim 23, wherein the classification data
comprises a location of the one or more structures, a model of the
one or more thermostats, a number of the one or more thermostats in
each of the one or more structures, a type of the HVAC system of
the one or more structures, a temperature profile of the one or
more structures, or a combination thereof.
25. The method of claim 23, wherein the classification data
comprises a total number of occupants of the one or more
structures, ages of the occupants of the one or more structures,
genders of the occupants of the one or more structures, ethnicities
of the occupants of the one or more structures, group affiliations
of the occupants of the one or more structures, income range of the
occupants of the one or more structures, or a combination
thereof.
26. The method of claim 23, comprising dividing the plurality of
temperature setpoints into groups based on the classification data;
and wherein constructing the at least one signature-based
temperature model comprises constructing a different
signature-based temperature model for each group.
27. A system, comprising: a memory storing occupant activity data
and a plurality of temperature setpoints of one or more temperature
setpoint schedules; a processor configured to: determine a value of
a model input that occurred at or near a time associated with a
particular temperature setpoint of the plurality of temperature
setpoints, determine a piece of the occupant activity data that
occurred at or near the time of the particular temperature
setpoint, and determine a correlation between the value of the
model input, the piece of the occupant activity data, and a
temperature associated with the particular temperature setpoint;
and construct a portion of a signature-based temperature model
based on the determined correlation.
28. The system of claim 27, wherein the processor is configured to
determine the value of the model input from one or more respective
online resources via a network connection.
29. The system of claim 27, wherein the processor is configured to
construct the portion of a signature-based by training an
artificial intelligence agent using the determined correlation.
30. The system of claim 27, wherein the correlation comprises a
probability that the value of the model input and the portion of
the occupant activity data will result in the temperature
associated with the particular temperature setpoint being
implemented.
31. The system of claim 27, wherein the portion of the
signature-based temperature model comprises a function configured
to: receive a current value of the model input, receive a current
piece of the occupant activity data, and output a temperature value
using the determined correlation.
32. The system of claim 31, wherein the portion of the
signature-based temperature model has an associated weight, and
wherein the processor is configured to multiply the temperature
value output by the portion of the signature-based temperature
model by the associated weight when implementing a signature-based
temperature program based on the signature-based temperature
model.
33. The system of claim 27, wherein the system is a thermostat
service configured to receive the plurality of temperature
setpoints and the occupant activity data from one or more
thermostats.
34. The system of claim 33, wherein the processor is configured to
send the signature-based temperature model to the one or more
thermostats to implement a signature-based temperature program
based on the signature-based temperature model.
35. The system of claim 27, wherein the model input comprises
weather data, emergency data, closure notification data, utility
data, economic data, traffic data, or transportation data.
36. The system of claim 27, wherein the system is a thermostat
controlling a heating, ventilation, and cooling (HVAC) system.
37. The system of claim 36, wherein the processor is configured to
implement at least the portion of the signature-based temperature
model to control the HVAC system.
38. The system of claim 36, wherein the system comprises one or
more sensors communicatively coupled to the processor, wherein the
one or more sensors are configured to collect the occupant activity
data for storage in the memory.
39. The system of claim 38, wherein the one or more sensors
comprise thermal sensors, motion sensors, light sensors,
electromagnetic sensors, sound sensors, vibration sensors, gas
sensors, or combinations thereof.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 61/917,533, entitled "SYSTEMS AND METHODS FOR
SIGNATURE-BASED THERMOSTAT CONTROL," filed Dec. 18, 2013, which is
herein incorporated by reference in its entirety for all
purposes.
BACKGROUND
[0002] The present disclosure relates generally to heating,
ventilation, and cooling (HVAC) systems that are communicatively
coupled to, and controlled by, programmable thermostats. Such
programmable thermostats generally include a memory that stores
instructions, as well as a processor that executes the stored
instructions, in which the instructions dictate suitable control
signals that should be supplied to the HVAC system to implement a
particular temperature program. More specifically, the present
disclosure relates to temporarily or permanently modifying a
time-based temperature program of a programmable thermostat based
on changes in the type or degree of observed or anticipated
occupant activity. Additionally, the present disclosure relates to
determining and implementing an activity-based temperature program
that a programmable thermostat may use to control a HVAC system
based on a type or degree of observed or anticipated occupant
activity, rather than according to a time-based schedule.
[0003] This section is intended to introduce the reader to various
aspects of art that may be related to various aspects of the
present disclosure, which are described and/or claimed below. This
discussion is believed to be helpful in providing the reader with
background information to facilitate a better understanding of the
various aspects of the present disclosure. Accordingly, it should
be understood that these statements are to be read in this light,
and not as admissions of prior art.
[0004] Thermostatically controlled HVAC systems are ubiquitous in
both residential and commercial structures. Classical
non-programmable thermostats generally allow a user to specify a
single setpoint temperature, for example, using a dial of an analog
thermostat or the pushbuttons of a digital thermostat. In
operation, the thermostat controls the HVAC system in a manner that
maintains the current ambient temperature within a predetermined
maintenance band around the setpoint temperature. This maintenance
band usually includes a lower maintenance band temperature equal to
the setpoint temperature minus about one degree Fahrenheit and an
upper maintenance band temperature equal to the setpoint
temperature plus about one degree Fahrenheit. In a heating mode of
operation, the thermostat may activate the HVAC system heating
function to heat the structure when the ambient temperature falls
below the lower maintenance band temperature, and then may
deactivate the HVAC system heating function once the ambient
temperature rises above the upper maintenance band temperature. In
a cooling mode of operation, the thermostat may activate the HVAC
system cooling function to cool the structure when the ambient
temperature rises above the upper maintenance band temperature, and
then may deactivate the HVAC system cooling function once the
ambient temperature falls below the lower maintenance band
temperature.
[0005] Certain programmable digital thermostats may include a clock
element and may provide an interface to enable a user to provide a
particular schedule for operating the HVAC system. For example,
certain programmable digital thermostats may allow a user to
specify temperature settings directed more toward occupant comfort
during certain parts of the day (e.g., between 7:00 AM-9:00 AM and
between 5:00 PM-10:00 PM), and to specify temperature settings
directed more toward energy savings during other parts of the day
(e.g., between 9:00 AM-5:00 PM and 10:00 PM-7:00 AM). By way of
example, such a programmable digital thermostat may allow a user to
specify, for the winter season, a heat setpoint temperature of 75
degrees Fahrenheit (.degree. F.) between 7:00 AM-9:00 AM (e.g., for
greater occupant comfort while the occupant gets out of bed and
gets ready for work), a heat setpoint temperature of 62.degree. F.
between 9:00 AM-5:00 PM (e.g., for greater energy savings while the
occupant is away at work), a heat setpoint temperature of
73.degree. F. between 5:00 PM-10:00 PM (e.g., for greater occupant
comfort as the occupant is at home during the evening), and a heat
setpoint temperature of 66.degree. F. between 10:00 PM-7:00 AM
(e.g., for greater energy savings while the occupant is
asleep).
[0006] Additionally, HVAC systems may be generally responsible for
a substantial portion of the power consumption of a residential or
commercial structure, especially in locales with extreme hot or
cool environments. In certain situations, this power consumption
may be further exacerbated when an occupant persistently adjusts
the thermostat based on his or her perception of the internal
temperature of the structure. Accordingly, it may be desirable to
reduce an amount of time that the HVAC system is actively heating
or cooling the structure in order to reduce power consumption of
the HVAC system while still addressing the temperature preferences
of the occupant.
SUMMARY
[0007] Certain embodiments commensurate in scope with the
originally described subject matter are summarized below. These
embodiments are not intended to limit the scope of the claims, but
rather these embodiments are intended only to provide a brief
summary of possible examples in accordance with the present
disclosure. Indeed, the present disclosure may encompass a variety
of forms that may be similar to or different from the embodiments
set forth below.
[0008] Present embodiments are directed toward systems and methods
for constructing and implementing temperature programs to control
the operation of a HVAC system of a structure. In particular,
present embodiments enable the implementation of a signature-based
temperature program in which a suitable temperature to implement is
not selected based on a schedule, but rather is selected based on
one or more pieces of information that are indicative of the
current state of the environment associated with the structure
and/or the current state of one or more occupants of the structure.
As used herein, a "signature-based temperature program" is a
temperature program in which various pieces of current environment
and/or occupant state information are provided as inputs to a
signature-based temperature model and, as a result, the desired
temperature is output from the signature-based temperature model.
As used herein, a "signature-based temperature model" is a
statistical or probability model that correlates desired HVAC
temperature preferences with a body of historical environment
and/or occupant activity information. Accordingly, the
signature-based temperature program determines a suitable
temperature to implement based on a particular set of current model
input values (i.e., a signature) and/or current occupant activity
measurements, rather than based on a time-based schedule.
[0009] As discussed below, present embodiments enable a processor
to generate a signature-based temperature model using data gleaned
from a number of sources, including existing time-based temperature
programs (i.e., temperature setpoint schedules) and various pieces
of historical environment and/or occupants state information (i.e.,
model inputs) for use in generating the signature-based temperature
model. For example, in certain embodiments, the processor may
collect temperature setpoint schedules from one or more thermostats
of one or more structures. Further, the processor may receive
various pieces of information (i.e., model inputs) that describe
past events or conditions that may have directly or indirectly
affected occupant temperature preferences, such as occupant
activity data, weather/emergency conditions, utility price and
performance conditions, stock market conditions, and so forth,
along with the times that the events or conditions occurred.
[0010] Accordingly, in certain embodiments, the processor may
generate the signature-based temperature model by correlating the
temperatures preferences of one or more time-based temperature
programs with the various historical values of the model inputs,
based on the time of occurrence, and identifying one or more
trends. As such, present embodiments enable the generation of a
signature-based temperature model that embodies determined
statistical or probabilistic correlations between existing
temperature preferences and various model input values. Further,
present embodiments enable the implementation of a signature-based
temperature program in which a suitable temperature may be
determined at any point in time by inputting current values for the
model inputs (e.g., current occupant activity, current weather
conditions, current utility price, etc.) to the signature-based
temperature model.
DRAWINGS
[0011] These and other features, aspects, and advantages of the
present disclosure will become better understood when the following
detailed description is read with reference to the accompanying
drawings in which like characters represent like parts throughout
the drawings, wherein:
[0012] FIG. 1 is a schematic of a structure having a heating,
ventilation, and cooling (HVAC) system, in accordance with an
embodiment of the present approach;
[0013] FIG. 2 is a schematic of an embodiment of a HVAC system;
[0014] FIG. 3 is a diagram illustrating certain internal components
and certain data inputs of a thermostat controlling the HVAC
system, in accordance with an embodiment of the present
approach;
[0015] FIG. 4 is a flow diagram illustrating an embodiment of a
process whereby the thermostat may generate a time-based
temperature program;
[0016] FIG. 5 is an embodiment of the time-based temperature
program generated by the thermostat according to the process of
FIG. 4;
[0017] FIG. 6 is a diagram illustrating collection and transmission
of thermostat data by a plurality of thermostats, in accordance
with an embodiment of the present approach;
[0018] FIG. 7 is a diagram illustrating inputs utilized in the
generation of a signature-based temperature model, in accordance
with an embodiment of the present approach;
[0019] FIG. 8 is a flow diagram illustrating an embodiment of a
process by which a processor of a thermostat may construct a
signature-based temperature model;
[0020] FIG. 9 is a flow diagram illustrating an embodiment of a
process by which a processor of a thermostat service may construct
one or more signature-based temperature models using thermostat
data from a plurality of thermostats; and
[0021] FIG. 10 is a flow diagram illustrating an embodiment of a
process by which the processor of the thermostat may implement a
signature-based temperature program based on a generated
signature-based temperature model.
DETAILED DESCRIPTION
[0022] One or more specific embodiments will be described below. In
an effort to provide a concise description of these embodiments,
not all features of an actual implementation are described in the
specification. It should be appreciated that in the development of
any such actual implementation, as in any engineering or design
project, numerous implementation-specific decisions must be made to
achieve the developers' specific goals, such as compliance with
system-related and business-related constraints, which may vary
from one implementation to another. Moreover, it should be
appreciated that such a development effort might be complex and
time consuming, but would nevertheless be a routine undertaking of
design, fabrication, and manufacture for those of ordinary skill
having the benefit of this disclosure. The subject matter of the
instant disclosure is related to the subject matter of the
following commonly assigned applications, each of which is
incorporated by reference herein: U.S. Ser. No. 13/632,041 filed
Sep. 30, 2012 (Ref No. NES0162-US); U.S. Ser. No. 13/632,070 filed
Sep. 30, 2012 (Ref No. NES0234-US); and U.S. Ser. No. 13/864,929
filed Apr. 17, 2013 (Ref. No. NES0334-US).
[0023] When introducing elements of various embodiments of the
present disclosure, the articles "a," "an," and "the" are intended
to mean that there are one or more of the elements. The terms
"comprising," "including," and "having" are intended to be
inclusive and mean that there may be additional elements other than
the listed elements. Additionally, it should be understood that
references to "one embodiment" or "an embodiment" of the present
disclosure are not intended to be interpreted as excluding the
existence of additional embodiments that also incorporate the
recited features.
[0024] As used herein the term "HVAC" includes systems providing
both heating and cooling, heating only, cooling only, as well as
systems that provide other occupant comfort and/or conditioning
functionality such as humidification, dehumidification and
ventilation. As used herein the term "residential" when referring
to an HVAC system means a type of HVAC system that is suitable to
heat, cool and/or otherwise condition the interior of a building
that is primarily used as a single family dwelling. An example of a
cooling system that would be considered residential would have a
cooling capacity of less than about 5 tons of refrigeration (1 ton
of refrigeration=12,000 Btu/h). As used herein the term "light
commercial" when referring to an HVAC system means a type of HVAC
system that is suitable to heat, cool and/or otherwise condition
the interior of a building that is primarily used for commercial
purposes, but is of a size and construction that a residential HVAC
system is considered suitable. An example of a cooling system that
would be considered residential would have a cooling capacity of
less than about 5 tons of refrigeration. As used herein the term
"thermostat" means a device or system for regulating parameters
such as temperature and/or humidity within at least a part of an
enclosure. The term "thermostat" may include a control unit for a
heating and/or cooling system or a component part of a heater or
air conditioner. As used herein the term "thermostat" can also
refer generally to a versatile sensing and control unit (VSCU unit)
that is configured and adapted to provide sophisticated,
customized, energy-saving HVAC control functionality while at the
same time being visually appealing.
[0025] Additionally, as used herein in the context of thermostat
schedules, the term "temperature setpoint" is used to describe
piece of data stored in a memory of the thermostat that has an
associated temperature (e.g., the temperature that the thermostat
seeks to provide) and an associated time (e.g., the time at which
the temperature setpoint should take effect). As such, the term
"implement", as used herein with respect to a temperature setpoint,
generally refers to the thermostat controlling the HVAC system
(e.g., activating or deactivating one or more heating or cooling
stages of the HVAC system) in a suitable manner to provide the
temperature associated with the temperature setpoint. It may also
be appreciated that the term "temperature setpoint," as used herein
in the context of a signature-based temperature program, may
describe a piece of data stored in a memory that includes an
associated temperature (e.g., the temperature that the thermostat
seeks to provide) but lacks an associated time, since the
associated temperature may be responsively implemented by the HVAC
system based on one or more model input values.
[0026] As mentioned above, it may be generally desirable to reduce
power consumption of a HVAC system of a particular structure. One
way of generally achieving this reduction is to implement a
temperature program that is based on a statistical model, instead
of a time-based schedule. For example, as presently disclosed, a
processor of a thermostat may control a HVAC system of a structure
according to a signature-based temperature program that is based on
a signature-based temperature model, rather than according to a
time-based temperature program. As discussed in detail below, in
certain embodiments, the signature-based temperature model may be
constructed based on thermostat data (e.g., data collected by a
single thermostat, by a group of nearby or similar thermostats, or
by other sensors) as well as historical values of plurality of
model inputs. As discussed below, the thermostat data includes
occupant temperature preferences (e.g., temperature setpoint
schedules), and also may include occupant activity data and
classification data as well. As also discussed below, the plurality
of model inputs may generally describe certain events or conditions
that might directly or indirectly influence occupant temperature
preferences (e.g., outdoor temperature, outdoor humidity, sunlight
levels, air quality, time of day, time of month, time of year,
price of power, stock market performance, and so forth).
Accordingly, the thermostat may utilize the signature-based
temperature model to statistically determine and implement
temperature setpoints that are derived from the temperature
preferences of occupants (e.g., occupants of the structure and/or
occupants of nearby or similar structures) relative to current
occupant activities (e.g., particular types or degrees of occupant
activity and/or changes between different types or degrees of
occupant activity) and/or current values of other model inputs. As
such, the disclosed signature-based temperature program may enable
the processor of the thermostat to, with little to no training,
implement a signature-based temperature program that is based on
the temperature preferences that other users (e.g., occupants of
nearby or similar structures) tend to have in response to or during
certain events or conditions, as described by occupant activity
measurements and/or the current values of the various model
inputs.
[0027] With the foregoing in mind, FIG. 1 illustrates an example of
a structure 10 having a HVAC system 20, in accordance with
embodiments of the present approach. In particular, the structure
10 illustrated in FIG. 1 is a "smart" residential dwelling that
includes a number of "smart" features (e.g., sensors, processors,
automation devices, and so forth) that may enable devices of the
structure 10 (e.g., lights, door locks, appliances, etc.) to
operate in an automated manner. For example, the structure 10 may
include a controller 15, which may be part of a home automation
system for controlling one or more features of the structure 10.
For example, the illustrated "smart" structure 10 may include a
plurality of sensors 12 (e.g., sensors 12A-J, discussed in greater
detail below) and a plurality of light fixtures 14 (e.g., light
fixtures 14A-14J) that may be communicatively coupled to a
controller 15. Further, the controller 15 may include a processor
programmed to utilize the inputs from the plurality of sensors 12
to determine occupancy of a particular portion of the structure 10
and programmed to only activate a portion of the plurality of light
fixtures 14 that are positioned in the occupied portion of the
structure 10. It may be appreciated that the light fixtures 14 are
merely provided as an example, and that, in certain embodiments,
the controller 15 may be programmed to control any feature (e.g.,
lighting, sound systems, window shades, appliances, etc.) of the
structure 10 in an automated fashion. In certain embodiments, the
controller 15 may be implemented on a portable computing device,
such as a tablet or a smart phone. In certain embodiments, the
controller 15 may be implemented by a wall-mounted device (e.g.,
like the controller 15 of FIG. 1), in part, and by a portable
computing device, such as a tablet of smart phone, in part. As set
forth below, in certain embodiments, thermostats 16A-B that control
the HVAC system 20 may be communicatively coupled to or integrated
with one or more features of the "smart" structure 10, such as the
sensors 12A-J and/or the controller 15, to receive information
regarding different types or levels of occupant activity for the
structure 10. For example, in certain embodiments, the thermostats
16 may determine how to control the HVAC system 20 based in part on
input from the controller 15, while, in other embodiments, the
thermostats 16 and the controller 15 may cooperate to jointly
decide how to control the HVAC system 20 of the structure 10. In
certain embodiments, the presently disclosed approach may be
implemented in commercial and residential structures that lack one
or more of the described "smart" features (e.g., sensors 12A-J,
controller 15). For such embodiments, as discussed below, the
thermostats (e.g., thermostats 16A and/or 16B) may themselves
include one or more sensors (e.g., passive infra-red sensors,
ultrasound sensors, vibration sensors, etc.) that are able to
detect or measure different types or levels of occupant activity
for the structure 10. Examples of thermostats including such
sensors for detecting occupant activity are disclosed in U.S. Pat.
No. 8,622,314, which is incorporated by reference herein in its
entirety for all purposes. For example, the thermostats 16 may be
Learning Thermostats.RTM. and the sensors 12 may be Nest
Protect.RTM. smoke detectors, both available from Nest Labs of Palo
Alto, Calif.
[0028] The HVAC system 20 illustrated in FIG. 1 includes an
interior unit 22 and an exterior unit 24 and is controlled by the
thermostats 16A and 16B, which are illustrated as wall-mounted
devices. As discussed in detail below with respect to FIG. 2, the
interior unit 22 and the exterior unit 24 of the HVAC system 20
include a number of components that cooperate to condition (e.g.,
cool, heat, humidify, dehumidify, ventilate, etc.) interior
portions of the structure 10 based on control signals supplied by
the thermostats 16A and 16B. It should be appreciated that the HVAC
system 20 illustrated in FIG. 1 is merely provided as an example,
and the present approach is also applicable to more complicated
HVAC systems having any suitable number of thermostats 16, interior
units 22, exterior units 24 to manage the temperature of the
structure 10. Further, it should be appreciated that, while certain
descriptions and examples set forth below may describe the
activities of a single occupant for simplicity sake, embodiments of
the present approach are applicable to any structure 10 having one
or more occupants at particular times.
[0029] In addition to the HVAC system 20, the "smart" residential
structure 10 illustrated in FIG. 1 may include other systems (e.g.,
automation systems, security systems, power management systems, and
so forth) that may be communicatively coupled to the HVAC system 20
to better manage the temperature of the structure 10. For example,
as set forth above, in certain embodiments, the controller 15 may
be a controller for a home automation system that may manage the
lighting (e.g., light fixtures 14) throughout the structure 10. The
home automation systems may, additionally or alternatively, include
other "smart" appliances (e.g., an automated pool heater 25, an
automated garage door system 26, and automated lawn watering system
27, automated front door locking devices 28, and so forth) to allow
an occupant to monitor, manage, or control systems and components
of the structure 10. In other embodiments, the controller 15 may,
additionally or alternatively, be a controller for a home security
system that may include the sensors 12 as well as other security
features (e.g., window sensors 30, door entry sensors 32, and so
forth) to allow an occupant to manage security features of the
structure 10. Additionally, in certain embodiments, the controller
15 may be communicatively coupled to the thermostats 16A and 16B to
exchange information about activities of occupants and/or devices
of the structure 10.
[0030] Further, the "smart" residential structure 10 illustrated in
FIG. 1 has a power management system that includes the "smart"
power meter 34, which may provide information to the "smart"
appliances associated with the structure 10 (e.g., the thermostats
16, refrigerator 36, dishwasher 38, electric vehicle (EV) 39, EV
charging station 40, water heater 42, exercise equipment 43, washer
44, dryer 46, television 47, or any other suitable "smart"
appliance of the structure 10) regarding the current price of
power, the current load on the power grid, power usage statistics
of the structure 10, and so forth, such that the "smart" appliances
may operate only at particular times to reduce power costs and/or
improve overall efficiency of the structure 10. Accordingly, one or
more components of each of the described control systems (e.g., a
home automation system, a home security system, etc.) of the
structure 10 may be communicatively coupled (e.g., via a wireless
or wired networking interface, discussed in detail below) to at
least one thermostat 16 of the HVAC system 20 to provide data
inputs to a processor of the thermostat 16 to be used in
determining and implementing temperature setpoints, as discussed in
detail below. It may be appreciated that, in certain embodiments,
one or more of the components of the structure 10, such as the
sensors 12 discussed in detail below, may be directly coupled to,
or part of, the HVAC system 20 (e.g., rather than being part of
another system of the structure 10 that is indirectly coupled to
the HVAC system 20). Furthermore, the sensors 12 may be "stand
alone" devices, as depicted, or the sensors 12 may be incorporated
into any of the devices used in the "smart" residential structure
10, such as smoke detectors, thermostats 16, refrigerator 36,
dishwasher 38, EV 39, EV charging station 40, water heater 42,
etc.
[0031] With the foregoing in mind, the sensors 12 distributed
throughout the structure 10 illustrated in FIG. 1 may each include
a number (e.g., 1, 2, 3, 4, 5, or more) sensing devices capable of
performing one or more measurements of the surrounding environment
in and around the structure 10. As set forth in detail below, the
measurements performed by these sensors 12 provide inputs (e.g.,
directly or indirectly) to the HVAC system 20, and potentially
other systems (e.g., automation systems, security systems, etc.) of
the structure 10, that may be used to determine how many occupants
may be in the structure 10, where occupants may be located within
the structure 10, and what types and/or levels of activity these
occupants may be performing For example, in certain embodiments,
the sensors 12 may include a thermal or acoustic motion sensing
device capable of detecting occupancy (e.g., presence of one or
more occupants) and/or occupant activities (e.g., movement or
actions of the one or more occupants). By further example, in
certain embodiments, the sensors 12 may include an acoustic
monitoring device, such as a microphone, capable of measuring
sounds of different intensities and/or frequencies as a measure of
occupancy and/or particular occupant activities within portions of
the structure 10. In certain embodiments, the acoustic monitoring
device of the sensors 12 may be paired with an ultrasonic emitter
to enable the acoustic monitoring device to perform sonar
measurements as a measure of occupancy and/or occupant activities
within portions of the structure 10.
[0032] By further example, in certain embodiments, the sensors 12
may include visible or infra-red (IR) light sensing devices (e.g.,
passive IR sensors) capable of measuring occupancy and/or occupant
activity within portions of the structure 10. For example, in
certain embodiments, the sensors 12 may include an IR sensing
device that is capable of measuring a temperature of an occupant to
provide indication of a level of activity of the occupant. In
certain embodiments, the sensors 12 may include cameras (e.g.,
visible light and/or IR cameras, such as cameras of a security
system) capable of capturing visual images of the occupant that may
analyzed (e.g., by the controller 15 or the thermostats 16) to
determine occupancy and/or occupant activities in portions of the
structure 10. For example, in certain embodiments, video or image
data from such visible light or IR camera sensors 12 may be
analyzed using any number of facial, voice, and/or gait recognition
algorithms or techniques.
[0033] In certain embodiments, the sensors 12 may include vibration
sensing devices capable of subtle movements within the structure 10
as an indication of occupancy and/or occupant activity within
portions of the structure 10. The sensors 12 may, in certain
embodiments, include air pressure sensors capable of measuring air
pressure changes (e.g., caused by opening and closing of doors of
the structure 10, caused by respiration and/or motion of the
occupants) as an indication of occupancy and/or occupant activity
within portions of the structure 10. In certain embodiments, the
sensors 12 may include gas analysis devices capable of detecting
presence or levels of smoke, carbon monoxide, water vapor, methane,
and/or carbon dioxide, which may provide a measure of occupancy
and/or particular occupant activities (e.g., cooking, exercising,
smoking, etc.) within portions of the structure 10. Additionally,
in certain embodiments, the sensors 12 may include temperature
sensing devices (e.g., thermocouples or IR sensors) capable of
measuring one or more temperatures within the structure 10, which
may also provide a measure of occupancy and/or occupant activity
within portions of the structure 10.
[0034] Additionally, in certain embodiments, the sensors 12 may
include flow sensing devices that may, for example, be coupled to
the plumbing of shower 54, the bathroom sink 56, the washing
machine 58, the dishwasher 38, the kitchen sink 58, the water
heater 40, toilets (not shown), and so forth, to provide a measure
of occupancy and/or occupant activities (e.g., bathing, washing
dishes, washing clothes, flushing toilets, etc.) within particular
portions of the structure 10. Similarly, in certain embodiments,
the sensors 12 may include flow sensing devices coupled to one or
more natural gas conduits of the structure 10 (not show) that, for
example, may be coupled to the range 60, the water heater 40, the
dryer 46, and/or other components of the structure 10. Such flow
sensors may also provide a measure of occupancy and/or certain
occupant activities (e.g., bathing, washing dishes, cooking, etc.)
within particular portions of the structure 10
[0035] In certain embodiments, the sensors 12 may include
electromagnetic sensing devices that are capable of measuring
electromagnetic signals. For example, in certain embodiments, an
electromagnetic sensing device of the sensors 12 may measure the
electrical magnetic interference (EMI) or electrical noise
generated by the operation of electronic devices, which may provide
a measure of occupancy and/or occupant activities (e.g., drying
hair with a hair dryer, preparing toast using a toaster oven,
heating coffee in a microwave, and so forth) in a portion of the
structure 10. In certain embodiments, the electromagnetic sensing
device may measure an EMI signal generated in free space (e.g.,
through the air) or an EMI signal generated within the electrical
circuit (e.g., electrical noise on the circuit being used) a result
of the operation of the electrical device in a portion of the
structure 10. In certain embodiments, the sensors 12 may include
electromagnetic sensing devices capable of measuring the load on an
electrical circuit of the structure, which may provide another
measure of occupancy and/or occupant activities (e.g., activation
of the dryer 46, activation of the dishwasher 38, activation or
deactivation of the EV charging station 40) in a portion of the
structure 10.
[0036] By further example, in certain embodiments, the
electromagnetic sensing devices of the sensors 12 may measure one
or more attributes of a wireless communication signal, such as a
wireless signal generated by a cellular phone 48, a computer 50,
and/or a router 52 (e.g., a wireless router or wireless access
point). By specific example, the sensors 12 may detect movement of
the cellular phone 48 throughout the structure 10 (e.g., in the
pocket or purse of the occupant) by measuring a progressively
changing intensity of one or more wireless communication signals
(e.g., cellular signals, WiFi data signals, Bluetooth.RTM. signals,
etc.) emitted by the cellular phone 48 as it is carried through the
structure 10 by the occupant. In certain embodiments, the sensors
12 may instead measure the totality of wireless communication
signals currently being generated as an indication of occupancy
and/or occupant activities (e.g., checking email, web surfing,
streaming media, etc.) within a portion of the structure 10. In
still other embodiments, the sensors 12 may continually or
intermittently monitor one or more wireless communication signals
(e.g., wireless signals generated by the router 52) and may provide
a measure of occupancy and/or occupant activity based on
distortions to these signals that are caused by the motion or
activity of the occupant. In another embodiment, the sensors 12 may
instead measure a total wireless signal or a total wireless signal
of a particular channel of a wireless network (e.g., 802.11 WiFi
channel 6) as an indication of occupancy or occupant activity
within the structure 10. It may be appreciated that such an
embodiment may enable a level of wireless network traffic to be
gauged by sensors 12 that are not necessarily attached to (e.g.,
authenticated with) the wireless network.
[0037] As mentioned above, the HVAC system 20 may generally
function to control the environment, such as the temperature and/or
humidity, inside the structure 10. FIG. 2 is a schematic
illustrating certain components of an embodiment of the HVAC system
20, which may be a residential or light commercial structure HVAC
system 20. As discussed above, one or more thermostats (e.g.,
thermostat 16A and thermostat 16B) are coupled directly or
indirectly to the HVAC system 20 to control its operation. By
specific example, each of the thermostats 16A and 16B may be a
Learning Thermostat.TM. available from Nest Labs of Palo Alto,
Calif. As illustrated in FIG. 2, a first or primary thermostat 16A
may be directly coupled to a control unit 70 of the internal unit
22 of the HVAC system 20 (e.g., via one or more signal wires 72),
such that the thermostat 16A may provide control signals to the
control unit 70 to initiate heating or cooling by the illustrated
HVAC system 20. Additionally, the embodiment of the HVAC system 20
illustrated in FIG. 2 includes a second or secondary thermostat 16B
that may be communicatively coupled to the primary thermostat 16A
(e.g., via wireless communication link 74) such that the primary
and secondary thermostats 16A and 16B may cooperate to determine
the appropriate control signals to be provided to the control unit
70 to affect heating or cooling of the structure 10.
[0038] Additionally, for embodiment of the HVAC system 20
illustrated in FIG. 2, the interior unit 22 of the HVAC system 20
includes cooling coils 76, heating coils 78, as well as a fan 80.
During operation, the fan 80 directs an air flow 82 to enter the
HVAC system 20 via the return air duct 84 and to traverse the
filter 86. From there, the fan 80 directs the air flow 82 into the
interior unit 22, across the heating coils 78 and cooling coils 76,
and out of the interior unit 22, where the airflow 82 is
subsequently distributed throughout the structure 10 by ducts 88
and registers 90. In the case of heating, the airflow 82 may be
warmed when moving across the heating coils 78 of the interior unit
22 of the HVAC system 20.
[0039] In the case of cooling, heat may be removed from the airflow
82 by the cooling coils 76 of the interior unit 22 and transferred
to a coolant disposed inside of the cooling coils 76. The coolant
may subsequently be directed to a heat exchanger 92 of the exterior
unit 24 of the HVAC system 20 via a first conduit 94. After cooling
within the heat exchanger 92, the coolant may subsequently be
directed to the compressor 96 of the exterior unit 24 of the HVAC
system 20 for compression. Then, the compressed coolant may be
directed back to the cooling coils 76 of the interior unit 22 via
the conduit 98 to once again cool the airflow 82. It should be
appreciated that the HVAC system 20 illustrated in FIG. 2 is
provided merely as an example and that other embodiments may
include additional components (e.g., humidifiers/dehumidifiers,
furnaces, air handlers, fans, and so forth) and/or additional
features (e.g., multi-stage heating and/or cooling) without
negating the effect of the present approach.
[0040] As set forth above, the measurements performed by the
sensors 12 may be consumed by the thermostat 16 of the HVAC system
20 to determine occupancy and/or occupant activities within
portions of the structure 10. In certain embodiments, the
thermostat 16 may continuously or intermittently determine, for
example, how many occupants may be present within the structure 10,
where the occupants may be located within the structure 10, and the
types or levels of the activities the occupants. In certain
embodiments, the thermostat 16 may also determine occupancy and/or
occupant activity using other data inputs, discussed below. As set
forth below, the thermostat 16 may later correlate this occupant
activity data, as well as other model inputs, with temperature
program data to construct a signature-based temperature model. With
the foregoing in mind FIG. 3 is a schematic illustrating certain
internal components of an embodiment of the thermostat 16 as well
as certain data inputs 110 that may be used by the thermostat
16.
[0041] The illustrated embodiment of the thermostat 16 of FIG. 3
includes a processor 112 configured to execute instructions (e.g.,
software or firmware) stored in a memory 114 to control operation
of the thermostat 16 and to generally manage the temperature of the
structure 10 according to the preferences of the occupant. The
thermostat 16 illustrated in FIG. 3 also includes communication
circuitry 116 (e.g., network interface circuitry) that, as
discussed in detail below, is configured to enable the thermostat
16 to communicate with other devices and systems. The illustrated
embodiment of the thermostat 16 also includes input devices 118
(e.g., knobs, wheels, touchscreens, buttons, dials, or other
suitable input devices) to enable a user to directly interface with
the thermostat 16 to provide, for example, occupant temperature
preferences. The illustrated thermostat 16 further includes display
circuitry 120 capable of presenting a graphical user interface
(GUI) to display information to the user. Further, the illustrated
thermostat 16 also includes a number of (e.g., 1, 2, 3, 4, or more)
sensors 122, which may, in certain embodiments, include any
combination of the sensors 12 discussed above that may be disposed
on or coupled to the thermostat 16. For example, in certain
embodiments, the sensors 122 may include one or more of the
temperature sensing devices, acoustic sensing devices, visible or
IR sensing devices, vibration sensing devices, air pressure sensing
devices, or EMI sensing devices discussed above.
[0042] The communication circuitry 116 of the thermostat 16 may
include various wired and/or wireless networking interfaces that
enable the thermostat 16 to receive other data inputs 110. For
example, the communication circuitry 116 may include 802.xx (e.g.,
802.11 a/b/g/n/ac) wireless networking interface to enable the
thermostat 16 to communicatively couple to the router 52, which may
be the central internet communication hub for the structure 10.
That is, the router 52 may host the computer network of the
structure 10 and may provide wired and/or wireless access to the
network, as well as the internet, for the devices of the structure
10. Accordingly, the communication circuitry 116 may enable the
thermostat 16 to interact with certain online resources 124 (e.g.,
online thermostat management resources, online temperature setpoint
schedule backup resources, historical temperature profile
information for the structure 10, and so forth) via its connection
to the router 52. It may be appreciated that the network
illustrated in FIG. 3 is merely provided as an example and that, in
other embodiments, the thermostat 16 may directly communicate with
the illustrated devices without the router 52 (e.g., in a wireless
mesh network, or similar topology).
[0043] Additionally, being coupled to the computer network of the
structure 10 (e.g., hosted by the router 52) may enable the
thermostat 16 interact with certain data inputs 110 (e.g., data
sources) also coupled to the router 52 in order to detect or
determine occupancy and/or occupant activity in the structure 10.
For example, as discussed above with respect to FIG. 1, in certain
embodiments the thermostat 16 may receive information from
automation systems 126, security systems 128, and/or power
management systems 130 to determine information relevant to
occupancy and/or occupant activity within portions of the structure
10. Also, as mentioned above, in certain embodiments the thermostat
16 may receive information directly from sensors 12 positioned
throughout the structure 10 relevant to occupancy and/or occupant
activity within portions of the structure 10. Further, one or more
of the "smart" appliances 132 of the structure 10 discussed above
may be also coupled to the router 52, which may enable the
thermostat 16 to determine information (e.g., modes of operation,
operation schedules, access or usage schedules, maintenance
schedules, and so forth) for these appliances that may be used to
determine or predict occupancy and/or occupant activity within
portions of the structure 10.
[0044] Further, as illustrated in FIG. 3, since the thermostat 16
is coupled to the router 52, the thermostat 16 may also detect the
presence or activity of certain devices communicating on the
computer network hosted by the router 52. For example, in certain
embodiments, the thermostat 16 may be able to detect network
traffic being generated by a computer 50, cell phone 48, television
47, video game consoles 136, streaming media devices 138, or any
other networked device of the structure 10, which may be used as a
measure of occupancy and/or occupant activity within portions of
the structure 10. By specific example, in certain embodiments, a
spike in network traffic by one or more of the devices listed above
may indicate that an occupant is actively using a device (e.g.,
television 47) that may be associated with a particular portion of
the structure 10 (e.g., the living room or den). Further, in
certain embodiments, the thermostat 16 may receive information
regarding the current location (e.g., global positioning system
(GPS) coordinates or cellular phone location coordinates) of the
occupant from one or more data inputs 110 (e.g., the cellular phone
48, the computer 50, a navigation system of the EV 39), which may
be use to determine present activity and predict future activity of
the occupant. For example, the thermostat 16 may determine that the
occupant is present or away or to predict when the occupant may
arrive at the structure 10 based on such occupant location
information.
[0045] Furthermore, being coupled to the computer network of the
structure 10 may also enable the thermostat 16 to interact with
certain data inputs 110 to predict future occupancy and/or occupant
activities in the structure 10. For example, in certain
embodiments, an occupant may enable the thermostat 16 to access
occupant schedule information from one or more data inputs 110. By
specific example, an occupant may maintain an agenda or schedule on
the computer 50, on the cellular phone 48, or using an online
resource 124, and the occupant may further grant the thermostat 116
access to the occupant's schedule on one or more of these devices
or resources. In certain embodiments, the thermostat 16 may be able
to access other occupant information from the computer 50, cellular
phone 48, and/or online resources 124, such as, for example, the
occupant's e-mails, notes, instant messages, to-do lists, or any
other suitable data source storing information relevant to
predicting future activities of the occupant. For example, in
certain embodiments, the thermostat 16 may access a Passbook.RTM.
app, or another suitable app or application of the cellular phone
48 storing event and travel ticket information, to glean
information about future outings and/or travels of the occupant. By
further example, in certain embodiments, the thermostat may access
one or more scheduled alarms of an alarm application of the
cellular phone 48 to glean information useful in predicting when
the occupant may wake the following day. Accordingly, the
thermostat 16 may utilize one or more of these resources to predict
future occupancy and/or occupant activity in the structure 10.
[0046] As mentioned above, the thermostat 16 is capable of
receiving occupant temperature preferences to construct a
time-based temperature program (e.g., a temperature setpoint
schedule) for the structure 10. As mentioned above and discussed in
greater detail below, while the presently disclosed signature-based
temperature program is generally independent of time (i.e., not a
time-based program), the disclosed signature-based temperature
model may be generated based on temperature preferences gleaned
from time-based temperature programs (e.g., temperature setpoint
schedules). FIG. 4 is a flow diagram illustrating an embodiment of
a process 150 by which the processor 112 of the thermostat 16 may
determine a temperature setpoint schedule based on a combination of
occupant temperature preferences and collected occupant activity
data. It should be appreciated that the illustrated process 150
represents one possibility for how a temperature setpoint schedule
may be constructed and, in other embodiments, other methods may be
used without negating the effect of the present approach. In
certain embodiments, the process 150 may incorporate one or more
features of the methods for automated control-schedule acquisition
set forth in U.S. Patent No. 8,630,740, which is incorporated by
reference herein in its entirety for all purposes.
[0047] With the foregoing in mind, the illustrated process 150 of
FIG. 4 begins with the processor 112 of the thermostat 16
activating learning mode (block 152). In certain embodiments, the
processor 112 may activate learning mode in response to user input
(e.g., received from input devices 118). In certain embodiments,
the processor 112 of the thermostat 16 may be preprogrammed to
begin operation in learning mode by default after installation.
While learning mode is activated, the processor 112 may receive
(block 154) user input at a particular time requesting a particular
temperature. In response, the processor 112 of the thermostat 16
provides control signals (block 156) the HVAC system 20 to adjust a
temperature of structure 10 according to the occupant temperature
preference. Further, the processor 112 stores (block 158) both the
temperature and the time associated with the user input in the
memory 114 for trend analysis. Additionally, in certain
embodiments, the processor 112 may also collect and store (block
160) occupant activity data around the time associated with the
user input for trend analysis.
[0048] Throughout learning mode operation, after each user input is
received, the processor 112 of the thermostat 16 analyzes (block
162) the time, temperature, and occupant activity data associated
with the current user input relative to the time, temperature, and
occupant activity data associated with previous user inputs and
attempt to identify trends. If the processor 112 identifies (block
164) a trend or correlation between the time, temperature, and
occupant activity data associated with the current user input and
the time, temperature, and occupant activity data associated with
previous user inputs, the processor 112 may create (block 166) or
modify a temperature setpoint of the temperature setpoint schedule
based on the correlation. For example, when the processor 112
identifies that the occupant has a tendency to request a
temperature of about 70.degree. F. at 4:30 PM every Monday, then
the processor 112 may create a temperature setpoint having an
associated temperature of 70.degree. F. and an associated time of
4:30 PM on Monday. Furthermore, in creating the temperature
setpoint, in certain embodiments, the processor 112 may utilize the
occupant activity data to identify occupant activity types or
degrees that occur around the time (e.g., just prior and just after
the time) associated with the temperature setpoint. As discussed in
greater detail below, this occupant activity data may be used to
define the parameters (e.g., exception time windows, occupant
activity indicators) for occupant activity-based exceptions for the
created temperature setpoint, as discussed in greater detail below.
As indicated by the arrows 165 and 167, blocks 152 through 166 may
be repeated with every user input that occurs while the thermostat
16 is in learning mode.
[0049] It may be appreciated that, in certain embodiments, the
learning mode of the processor 112 may be more aggressive for an
initial period of time in order for an initial temperature setpoint
schedule to be generated. For example, the processor 112 may
initially add a temperature setpoint or change a temperature
setpoint of the temperature program after only two instances of a
user requesting a particular temperature near the same time of day.
Then, after an initial temperature program is generated, the
learning mode of the processor 112 may be less aggressive and only
modify the temperature setpoint schedule after several deviations
(e.g., four or more deviations) from the current temperature
program are observed in the user inputs and/or the occupant
activity data. In certain embodiments, the thermostat 16 may remain
in learning mode and may continue learning the temperature
preferences of the occupant throughout operation, albeit in a less
aggressive manner than when the learning mode was initially
activated. In other embodiments, after a period of time (e.g., a
day, week, fortnight, or month) has passed, the processor 112 may
deactivate the learning mode of the thermostat 16. In still other
embodiments, the processor 112 may deactivate the learning mode of
the thermostat 16 in response to user input requesting such
deactivation.
[0050] FIG. 5 depicts a visual representation of an embodiment of a
temperature setpoint schedule 170 such as may be generated as an
output of the process 150 illustrated in FIG. 4. The temperature
setpoint schedule 170 illustrated in FIG. 5 includes the days of
the week 172 on the vertical axis and includes the hours of the day
174 on the horizontal axis. Additionally, the temperature setpoint
schedule 170 includes a number of temperature setpoints 176
indicating a particular time and a particular temperature
associated with each of the temperature setpoints 176. That is, as
illustrated in FIG. 5, the temperature setpoints 176 indicate
particular times using their respective positions in the
temperature setpoint schedule 170 and indicate particular
temperatures using the numerical values respectively displayed by
each of the illustrated temperature setpoints 176. Accordingly, as
the thermostat 16 is trained according to the temperature
preferences of the occupant during learning mode, the thermostat 16
may generate the temperature setpoint schedule 170 and may control
operation of the HVAC system 20 to implement the temperature
setpoint schedule 170 in an efficient manner.
[0051] With the foregoing in mind, it may be appreciated that, in
certain embodiments, a plurality of thermostats 16 may be disposed
in and control HVAC systems of structures distributed throughout
one or more regions (e.g., neighborhoods, towns, districts, cities,
states, counties, provinces, countries, hemispheres, time zones,
and so forth). Further, as discussed above with respect to FIG. 3,
each of these thermostats 16 may be communicatively coupled to one
or more online resources 124 (e.g., via the router 52). Turning now
to FIG. 6, in certain embodiments, the online resources 124 may
include an online thermostat service 180 implemented by one or more
processors 182 (e.g., of a server, a server farm, a cluster,
another suitable data processing configuration) executing
instructions stored in one or more memories 184. The thermostat
service 180 may interact with the thermostats 16 to provide
additional features or capabilities. For example, in certain
embodiments, the thermostat service 180 may enable backups of
occupant temperature preference data that may be used to restore
damaged or altered occupant temperature preferences or to provide
the occupant temperature preferences to a new thermostat for
implementation. In certain embodiments, the thermostat service 180
may also provide, for example, an interface whereby a user may
access and alter configurations and settings of a thermostat 16
using a networked device (e.g., computer 50 or cellular phone 48).
It should be appreciated that these features are merely provided as
examples of features of the thermostat service 180 and are not
intended to be limiting.
[0052] As illustrated in FIG. 6, an example neighborhood 186 is
presented in which a number of thermostats 16 are being used to
respectively control the HVAC systems of a number of structures.
For example, each of the illustrated thermostats 16 of FIG. 6 have
been trained (e.g., according to the process 150 illustrated in
FIG. 4) and are each implementing a respective temperature setpoint
schedule (e.g., similar to the temperature setpoint schedule 170)
based on occupant temperature preferences learned by the
thermostats 16 during operation. Furthermore, each of the
illustrated thermostats 16 of FIG. 6 may, as discussed above with
respect to FIG. 3, receive and store information regarding
occupancy and/or occupant activity from one or more sensors (e.g.,
sensors 12 and/or 112) and/or other data inputs 110. Accordingly,
as illustrated in FIG. 6, in certain embodiments, each of the
thermostats 16 may provide thermostat data 188 to the thermostat
service 180 (e.g., via an Internet connection, via router 52). It
should be appreciated that, in certain embodiments, portions of the
illustrated thermostat data 188 may be delivered to the thermostat
service 180 simultaneously or at different times on a continual or
intermittent basis.
[0053] As illustrated in FIG. 6, the thermostat data 188 may
include different types of data. For the illustrated embodiment,
the thermostat data 188 includes temperature program data 190,
which may include temperature setpoint schedules (e.g., temperature
setpoint schedule 170 illustrated in FIG. 5). In certain
embodiments, the temperature program data 190 may also include
other occupant temperature preferences including, for example,
learned exceptions to the temperature setpoint schedule or
temperature programs based on occupant activity. In addition to the
temperature program data 190, in certain embodiments, the
thermostat data 188 may also include occupant activity data 192
that is based on inputs received by each of the thermostats 16 from
one or more sensors (e.g., sensors 12 and/or 122) and/or other data
inputs 110 regarding occupancy and/or occupant activities within
the respective structures. For example, as illustrated in FIG. 6,
in certain embodiments the occupant activity data 192 of a
thermostat 16 may include one or more of: a number of occupants in
a structure, occupant locations within or without the structure,
occupant activity levels, computer network traffic, or other
suitable measures of occupancy and/or occupant activity. It may be
appreciated that the occupant activity data 192 may generally
correspond to the temperature program data 190. For example, in
certain embodiments, each piece of occupant activity data 192 may
correlate or correspond to a temperature setpoint of a temperature
setpoint schedule. That is, each piece of occupant activity data
may be collected at or near the time associated with a
corresponding temperature setpoint.
[0054] Additionally, as illustrated in FIG. 6, the thermostat data
188 may also include classification data 194 that may generally
define or describe aspects of the thermostat 16, the structure in
which the thermostat is installed, and/or one or more of the
occupants of the structure. As illustrated, in certain embodiments,
the classification data 194 provided by each thermostat 16 may
include a location (e.g., geographic region, community, city,
state, and/or global positioning system (GPS) coordinates) of the
structure in which the thermostat 16 is operating. In certain
embodiments, the classification data 194 may also include a
thermostat model (e.g., version 1, version 2, etc.), a number of
thermostats 16 in the structure 10 (e.g., 1, 2, 3, 4, or more),
and/or a type of HVAC system 20 being controlled (e.g., heating
only, cooling only, heating and cooling, multi-stage heating and/or
cooling, zoned systems, and so forth). Additionally, in certain
embodiments, the classification data 194 may include the
temperature profile of the structure 10, or a subportion or digest
thereof (e.g., a HVAC efficiency value that is based on the
temperature profile of the structure 10). Further, in certain
embodiments, the classification data 194 may include certain
occupant information (e.g., total number of occupants, ages,
genders, ethnicities, group affiliations, income range, and so
forth). As discussed below, the thermostat service 180 may, in
certain embodiments, logically divide the received thermostat data
188 into groups having similar classification data values when
constructing signature-based temperature models.
[0055] Accordingly, the one or more processors 182 of the
thermostat service 180 may receive thermostat data 188 from one or
more thermostats 16 and store the received data in the one or more
memories 184 for further processing. Turning to FIG. 7, the one or
more processors 182 of the thermostat service 180 may be configured
to generate a signature-based temperature model 196 based, at least
in part, on the received thermostat data 188. For example, in
certain embodiments, the thermostat service 180 may be configured
to generate a signature-based temperature model 196 based only on
the thermostat data 188 received from one or more thermostats 16.
That is, in certain embodiments, the one or more processors 182 of
the thermostat service 180 may correlate a portion of the occupant
activity data 192 with a portion of the temperature program data
190 to generate a signature-based temperature model 196. It may be
appreciated that, in other embodiments, the thermostat service 180
may not be used, and instead the processor 112 of the thermostat 16
may perform the actions of thermostat service 180 discussed below
(e.g., receive model inputs 200, correlate temperature program data
190 of that thermostat 16 with occupant activity data 192 and/or
model inputs 200) to locally generate the signature-based
temperature model 196.
[0056] For the embodiment illustrated in FIG. 7, the thermostat
service 180 receives one or more model inputs 200 that may also be
used in the generation of the signature-based temperature model
196. For the illustrated embodiment, the model inputs 200 belong to
one of three categories: weather/emergency model inputs 202,
utility model inputs 204, and other model inputs 206. It should be
appreciated that these three categories are merely provided as
non-limiting examples and that, in other embodiments, other model
inputs 200 may be used without negating an effect of the present
approach. It should also be appreciated that the thermostat service
180 may receive the model inputs 200 from one or more networked
computer resources (e.g., online websites or services 124) via a
network or Internet connection. It may further be appreciated that,
in certain embodiments, as the thermostat service 180 is collecting
model inputs 200 to correlate with the thermostat data 188, the
thermostat service 180 may limit the collection of model inputs 200
to events or conditions occurring at or near the location of a
particular thermostat 16 (e.g., based on the classification data
194 of the thermostat data 188 received from the thermostat 16)
and/or to events or conditions occurring at or near the time of a
particular temperature preference. In other words, in certain
embodiments, the values for the model inputs 200 used in the
generation of the signature-based temperature model 196 are
historical values that are associated with a particular time in the
past that is proximate to a time associated with one or more
temperature preferences of the thermostat data 188.
[0057] As illustrated in FIG. 7, in certain embodiments, the
weather/emergency model inputs 202 may be provided to the
thermostat service 180 by one or more weather resources (e.g.,
weather tracking websites, data feeds, or other suitable online
resources 124), emergency communication resources (e.g., online
Emergency Alert System (EAS) websites or data feeds), and/or
community resources (e.g., city or school websites) automatically
or upon request. These weather/emergency model inputs 202 may
include, for example, weather conditions 208 (e.g., temperature,
humidity, precipitation, air quality, sunlight, and so forth) at
particular times. Additionally, the weather/emergency model inputs
202 may include emergency warnings or alerts 210 for fires,
volcanic eruptions, blizzards, hurricanes, tornados, flash floods,
earthquakes, or other suitable emergency warnings or alerts at
particular times. Further, in certain embodiments, the
weather/emergency model inputs 202 may include closure
notifications 212 for schools, offices, roads, bridges, cities, and
so forth at particular times.
[0058] Historical values of the weather/emergency model inputs 202
may be correlated with temperature preferences contained in the
temperature program data 190 based on when and/or where the weather
or emergency event occurred. Certain weather/emergency model inputs
202 (e.g., weather conditions 208, such as temperature, humidity,
precipitation, air quality, and sunlight, and emergency warnings or
alerts 210, such as alerts of fires, volcanic eruptions, blizzards,
hurricanes, tornados, and flash floods) may directly affect
occupant temperature preferences since they directly affect (e.g.,
either providing a warming or cooling effect to) the environment
surrounding a structure. Other weather/emergency model inputs 202
(e.g., closure notifications 212 for schools, offices, roads,
bridges, cities) may affect the routine or schedule of the
occupant, which may indirectly affect occupant temperature
preferences. For example, a notification that a school is closed
for a day may indirectly affect the temperature preferences of, for
example, a mother staying home for the day with a number of
children as a result of the school closure.
[0059] In certain embodiments, the utility model inputs 204 may be
provided to the thermostat service 180 by one or more online
utility resources (e.g., websites, data feeds, or other suitable
online resources 124) automatically or upon request. For example,
the utility model inputs 204 illustrated in FIG. 7 may be provided
by a website of a power company. By specific example, the
illustrated utility model inputs 204 include power rates 214 at
particular times, peak power rates and times 216, and capacity/load
218 of the electrical network or grid at particular times. It may
be appreciated that the illustrated utility model inputs 204 are
merely provided as non-limiting examples and, in other embodiments,
other utility services (e.g., natural gas or water services) may
provide one or more utility model inputs 204 for construction of
the signature-based temperature model 196.
[0060] Historical values of the utility model inputs 204 may be
correlated with temperature preferences contained in the
temperature program data 190 based on when and/or where certain
values (e.g., certain rates, loads, peaks) occur for the utility
model inputs 204. For example, the power rate 219 at a particular
time may indirectly affect occupant temperature preferences based
on how much the occupant is willing to spend to operate the HVAC
system 20 at that particular time. By further example, the
capacity/load 218 of the electrical network at a particular time
may affect the temperature preferences of an occupant that is
environmentally conscious since the occupant may prefer the highest
levels of comfort only when the demand on the electrical network is
low, allowing the electrical network to operate in a more efficient
manner.
[0061] Additionally, in certain embodiments, other model inputs 206
may be provided to the thermostat service 180 by one or more online
resources (e.g., websites, data feeds, or other suitable online
resources 124) automatically or upon request. For the illustrated
embodiment, the other model inputs 206 include stock market prices
220, roadway traffic congestion 222, airport schedules 224, public
transportation schedules 226, and Internet outages 228. For
example, in certain embodiments, the thermostat service 180 may
receive prices of particular stocks, performance measures of entire
markets or exchanges, or other economic indicators (e.g., consumer
confidence index, consumer price index, volatility index,
unemployment rate, and so forth) at particular times. In certain
embodiments, the thermostat service 180 may receive roadway traffic
congestion information 222 indicating traffic jams, traffic
accidents, road construction, detours, road hazards, etc., at
particular times and/or particular locations. In certain
embodiments, the other model inputs 206 may include airport
schedules 224 and public transportation schedules 226 indicating
arrival and departure schedules, delays, cancelations, and so forth
at particular times and/or particular locations. Additionally, in
certain embodiments, the model inputs 206 may include information
regarding Internet outages 228, such as outages of specific
Internet services or resources (e.g., email, streaming media
service, video conferencing service) or complete outages (e.g., no
Internet traffic) at particular times.
[0062] Historical values of the other model inputs 206 may be
correlated with temperature preferences contained in the
temperature program data 190 based on when and/or where certain
values (e.g., certain stock prices, transportation schedule events,
or levels of traffic) that occur for the other model inputs 206.
For example, the other model inputs 206 may include the current
price of oil per barrel as an economic input whose fluctuation
typically precedes a similar fluctuation in the price of power. As
such, the price of oil per barrel at a particular time, for
example, may indirectly affect occupant temperature preferences as
it may indirectly affect the current and future budget of the
occupant. The roadway traffic congestion information 222 at a
particular time may indirectly affect the temperature preferences
of the occupant, for example, causing the occupant to prefer a
cooler temperature upon arriving home after traversing an onerous
traffic jam. Airport schedules 224 and/or public transportation
schedules 226 may indirectly affect the temperature preferences of
the occupant, for example, by affecting the schedule or routine of
the occupant. That is, if an airport schedule 224 delay results in
the occupant arriving home later than he or she usually arrives
during cold weather, the occupant may have a warmer temperature
preference than usual as a result. By further example, Internet
outages 228 at particular times may indirectly affect occupant
temperature preferences by reducing available sedentary activity
options (e.g., web surfing, viewing streaming media, online
gaming), which may cause the occupant to prefer cooler temperatures
while engaging in more energetic activities during the outages.
[0063] For the embodiment illustrated in FIG. 7, the thermostat
service 180 may receive historical values for the weather/emergency
model inputs 202, the utility model inputs 204, and/or the other
model inputs 206 from one or more online resources 124. Further, as
set forth above, each of the received historical values of the
model inputs 200 may be associated with a particular time in the
past and/or location that the event or condition described by the
particular value of the model input occurred. As such, in certain
embodiments, the thermostat service 180 may correlate in time the
historical values of the particular model inputs 200 with the
temperature program data 190 of the thermostat data 188. That is, a
processor (e.g., the one or more processors 182 of the thermostat
service 180 or the processor 112 of the thermostat 16) may use
proximity between a time associated with a particular model input
200 and a time associated with a temperature setpoint to correlate
the particular model input 200 with the particular temperature
setpoint. Further, as mentioned above, in certain embodiments, the
processor may, additionally or alternatively, correlate in time
particular temperature setpoints of the temperature program data
190 with occupant activity data 192 of the thermostat data 188.
That is, the processor may, additionally or alternatively, use
proximity between a time associated with a particular piece of
occupant activity data 192 (e.g., an occupant activity level) and a
time associated with a particular temperature setpoint to correlate
the particular piece of occupant activity data 192 and the
particular temperature setpoint. Subsequently, the processor (e.g.,
the one or more processors 182 of the thermostat service 180 or the
processor 112 of the thermostat 16) may generate the
signature-based temperature model 196 using the determined
correlations between the occupant temperature preferences (e.g.,
from the temperature setpoints of the temperature program data 190
of the thermostat data 188), occupant activities (e.g., from the
occupant activity data 192 of the thermostat data 188), and/or
historical values for the model inputs 200 (e.g., from
weather/emergency, utility, or other model input sources).
[0064] In certain embodiments, an occupant may be prompted to
answer a series of questions in a questionnaire or survey, either
using the thermostat 16 or a suitable computer user interface, upon
installing the thermostat 16. The received occupant answers may be
used to generate some or all of the aforementioned classification
data 194. Additionally, this questionnaire may also ask one or more
questions that may be used to rank the importance of different
model inputs 200 and/or occupant activities to the temperature
preferences of the occupants of the structure 10. For example, the
questionnaire may ask questions to identify which of the model
inputs 200 tend to have stronger effects on the occupant's
temperature preferences. By specific example, the questionnaire may
ask the occupant to rank how strong of an effect each of the
following has on his or her temperature preferences for the
structure: (1) current/future weather conditions; (2) season of the
year; (3) utility cost; (4) environmental friendliness; (5) current
price of fuel; (6) occupant activity level; and (7) occupant
travel/work/school schedule. For such embodiments, based upon the
occupant's answers, a relative weight (e.g., a weighting factor)
may be selected for each of the correlations of the signature-based
temperature model, as discussed in greater detail below.
[0065] It should be appreciated that the generated signature-based
temperature model 196 may generally be a statistically-based or
probability-based model. For example, in certain embodiments, the
signature-based temperature model 196 may include a
machine-learning agent (e.g., a Bayesian network, an artificial
neural network, or another suitable artificial intelligence agent)
trained using one or more known data sets such that it can perform
statistical or probability-based pattern matching on unknown future
data sets. For example, in certain embodiments, the signature-based
temperature model 196 may include a Bayesian network that is
trained using the determined correlations between the occupant
temperature preferences, occupant activities, and/or model inputs
200. As discussed below, after generating and/or training the
signature-based temperature model 196, the a processor (e.g., the
one or more processors 182 of the thermostat service 180 or the
processor 112 of the thermostat 16) may implement the
signature-based temperature model 196, which may determine a
temperature setpoint for the HVAC system 10 in response to future
inputs, namely future measures from the occupant activity data 192
and/or future values from the model inputs 200.
[0066] In other embodiments, the signature-based temperature model
196 may include a collection of instructions (e.g., a program or
module) that defines a set of signature-based temperature
setpoints, each having an associated temperature that represents a
statistical distribution of the temperatures associated with the
temperature setpoints 176 of the temperature setpoint schedule 170.
Further, in such an embodiment, each signature-based temperature
setpoint may be associated with at least one threshold value for
each measure of occupancy activity data 192 and/or each of the one
or more model inputs 200 determined by the processor (e.g., the one
or more processors 182 of the thermostat service 180 or the
processor 112 of the thermostat 16). For example, the threshold
values associated with each signature-based temperature setpoint
may define a target value, a lower limit, or an upper limit for
each measure of occupancy activity data 192 and/or each of the one
or more model inputs 200. Further, in such embodiments, the
threshold values associated with each signature-based temperature
setpoint may be determined based on a statistical analysis or
probability distribution of which values from the occupancy
activity data 192 and/or which values from the one or more model
inputs 200 tended to result in a user preference of a temperature
at or near the temperature associated with the signature-based
temperature setpoint. Accordingly, when a signature-based
temperature program is implemented (as discussed below with respect
to FIG. 11) using such an embodiment of a signature-based
temperature model 196, when the processor determines that a current
measure of occupant activity from the occupant activity data 192
and/or determines that a current value for the one or more model
inputs 200 meets or exceeds the threshold values associated with a
signature-based temperature setpoint, the processor may implement
the signature-based temperature setpoint in response.
[0067] In other embodiments, the signature-based temperature model
196 may include a collection of temperature curves (e.g., graphs,
equations, sets of points, look-up tables, or other suitable data
representations), wherein each temperature curve describes user
temperature preferences relative to a particular measure of
occupancy activity data 192 or a particular model input 200. That
is, in such embodiments, each temperature curve of the
signature-based temperature model 196 may be generated based on a
statistical analysis or probability distribution of which
temperatures users tend to prefer as the particular input (e.g.,
the value of the particular measure of occupancy activity data 192
or the value of the particular model input 200) is varied over a
range of potential values. Further, in such embodiments, each
temperature curve in the collection may be assigned a weight
depending on the particular measure of occupancy activity data 192
or the particular model input 200 described by the temperature
curve, and this weight may dictate how the temperature outputs of
each temperature curve may be combined to determine a single
temperature setpoint for the processor 112 of the thermostat 16 to
actually implement. For example, based on user input when the
signature-based temperature model is generated or implemented, the
processor (e.g., the one or more processors 182 of the thermostat
service 180 or the processor 112 of the thermostat 16) may assign a
relative weight to each of the temperature curves in the
collection. For such embodiments, when the signature-based
temperature program is implemented on the thermostat 16 based on
this signature-based temperature model 196, the processor 112 may
process each of the temperature curves in the collection,
determining from each temperature curve a temperature preference
output that corresponds to the value of the particular measure of
occupancy activity data 192 or the value of the particular model
input 200 described by the temperature curve. Then, for such an
embodiment, the processor 112 may first determine the products of
(e.g., multiply) the temperature preference output of each
temperature curve and the respectively weight assigned to each
temperature curve. The processor 112 may then sum these products to
determine a single temperature value, which may be implemented by
the thermostat 16 as a currently desired temperature setpoint.
[0068] With the foregoing in mind, FIG. 8 illustrates an embodiment
of a process 240 whereby a processor (e.g., the processor 112 of
the thermostat 16) may locally construct a signature-based
temperature model 196 from an existing temperature setpoint
schedule. The illustrated process 240 begins with the processor 112
receiving (block 242) user input (e.g., from input devices 118)
requesting the generation of a signature-based temperature model
from a temperature setpoint schedule 170 (e.g., generated according
to the process 150 of FIG. 4) having a plurality of temperature
setpoints 176. Additionally, in certain embodiments, the user may
also select or indicate inputs for the processor 112 to consider
when constructing the signature-based temperature model 196. For
example, in certain embodiments, the user may indicate that the
processor 112 should consider all, none, or particular portions of
the occupant activity data 192 (e.g., number of occupants) when
constructing the signature-based temperature model 196. By further
example, in certain embodiments, the user may indicate that the
processor 112 should consider all, none, or particular model inputs
200 (e.g., weather conditions 208, utility power rates 214, and
stock market prices 220) when constructing the signature-based
temperature model 196.
[0069] The illustrated embodiment of the process 240 continues with
the processor 112 determining (block 244) occupant activity data
192 at or near times associated with the temperature setpoints 176
of the temperature setpoint schedule 170. That is, the processor
112 of the thermostat 16 may retrieve from the memory 114 stored
occupant activity data 192 that is based on the measurements of one
or more sensors (e.g., sensors 12 and/or 112) and/or one or more
data inputs 110 associated with the thermostat 16. Further, the
processor 112 may determine particular occupant activity data 192
(e.g., number of occupants) from the memory 114 specifically at or
near the times respectively associated with each of the plurality
of temperature setpoints 176 of the temperature setpoint schedule
170. It may be appreciated that, in certain embodiments, the
processor 112 may not consider occupant activity data when
constructing signature-based temperature model 196, and,
accordingly, block 244 may be skipped altogether.
[0070] Next, the illustrated embodiment of the process 240
continues with the processor 112 determining (block 246) values for
one or more model inputs 200 at or near times associated with
temperature setpoints 176 of the temperature setpoint schedule 170.
That is, the processor 112 of the thermostat 16 may utilize
communication circuitry 116 to request and receive data from an
online resource 124 (e.g., a weather or emergency alert website, a
utility provider website, a stock market data feed) to determine
values for the one or more model inputs 200. Additionally, the
processor 112 may determine particular values for the model inputs
(e.g., weather alerts) from the online resources 124 specifically
at or near times respectively associated with each of the plurality
of temperature setpoints 176 of the temperature setpoint schedule
170 and/or specifically at or near the location of the structure 10
in which the thermostat 16 resides. It may be appreciated that, in
certain embodiments, the processor 112 may not consider model
inputs 200 when constructing signature-based temperature model 196,
and, accordingly, block 246 may be skipped altogether.
[0071] Continuing through the illustrated embodiment of the process
240, the processor 112 may then correlate (block 248) the
temperature setpoints 176 of the temperature setpoint schedule 170
with the determined occupancy activity data (e.g., from block 244)
and/or the determined values for the one or more model inputs 200
(e.g., from block 246). That is, the processor 112 may respectively
correlate a temperature of each temperature setpoint 176 with
occupancy activity data 192 and/or values determined for the one or
more model inputs 200 at or near the time associated with the
temperature setpoint 176. In other words, the thermostat 16 may
determine a connection between the temperature associated with a
temperature setpoint 176 and particular occupant activities or
activity levels (e.g., described by the occupant activity data 192
collected by the thermostat 16 at or near the time of the
temperature setpoint 176) and/or particular events or conditions
(e.g., described by the values of the one or more model inputs 200
at or near the time of the temperature setpoint 176).
[0072] Then, in the illustrated embodiment of the process 240, the
processor 112 may construct (block 250) the signature-based
temperature model 196 using the correlated data from block 248. For
example, as described above, in certain embodiments, the processor
112 may construct the signature-based temperature model 196 by
generating a machine-learning agent that is trained using the
correlations that the processor 112 determined between each of the
temperature setpoints 176 (e.g., the time and the temperature
associated with each of the temperature setpoints 176) and the
values determined for the occupancy activity data 192 and/or values
determined for the one or more model inputs 200. In other
embodiments, as discussed above, the processor 112 may implement
the signature-based temperature model 196 in the form of a set of
instructions (e.g., software module) that define signature-based
temperature setpoints having assigned threshold values for the
occupancy activity data 192 and/or the one or more model inputs 200
that are based on the correlated data from block 248. In still
other embodiments, as discussed above, the processor 112 may
construct the signature-based temperature model 196 in the form of
a number of weighted temperature curves (e.g., one for each measure
of the occupant activity data 192 and each of the one or more model
inputs 200) based on the correlated data from block 248.
[0073] As set forth above with respect to FIG. 7, in certain
embodiments, the thermostat data 188 used to generate the
signature-based temperature model 196 may be collected from a
plurality of thermostats 16. With this in mind, FIG. 9 illustrates
a process 260 whereby a processor (e.g., the one or more processors
182 of the thermostat service 180) may construct a number of
signature-based temperature models using the thermostat data 188
collected from multiple thermostats 16. The process 260 illustrated
in FIG. 9 begins with the processor receiving and storing (block
262) a set of thermostat data 188 from each of a plurality of
thermostats 16. Further, as illustrated in FIG. 9, in certain
embodiments, the set of thermostat data 188 collected from each
thermostat 16 includes classification data 194, temperature program
data 190, and occupant activity data 192. It may be appreciated
that, in other embodiments, the sets of thermostat data 188 may not
include classification data 194 and/or occupant activity data 192,
as discussed below.
[0074] Continuing through the process 260 illustrated in FIG. 9,
within each set of received thermostat data 188, the processor may
determine (block 264) values of occupant activity data 192
collected at or near times associated with the temperature program
data 190 (e.g., at or near times associated with temperature
setpoints 176 of the temperature setpoint schedule 170). It may be
appreciated that, in certain embodiments, the processor may not
receive occupant activity data 192 (e.g., in block 262), and, in
such embodiments, block 266 may be skipped altogether.
[0075] Next in the illustrated embodiment of the process 260, the
processor may determine (block 266) values for one or more model
inputs 200 at or near times associated with the temperature program
data 190 (e.g., temperature program data 190 from all of the
received sets of thermostat data 188). That is, the processor 112
of the thermostat 16 may utilize communication circuitry 116 to
request and receive data from an online resource 124 (e.g., a
weather or emergency alert website, a utility provider website, a
stock market data feed) to determine values for the one or more
model inputs 200 at or near times associated with the temperature
setpoints 176 of the temperature program data 190 from all of the
received sets of thermostat data 188.
[0076] Continuing through the process 260, the processor may then
correlate (block 268) the temperature setpoints 176 of the
temperature program data 190 from all of the received sets of
thermostat data 188 with the determined values of occupant activity
data 192 (e.g., in block 264) and/or the determined values of the
one or more model inputs 200 (e.g., in block 266). That is, the
processor may respectively correlate a temperature associated with
each received temperature setpoint 176 with the values of the
occupancy activity data 192 and/or the values of the one or more
model inputs 200 at or near the time associated with the
temperature setpoint 176. In other words, the processor may
determine a connection between the temperature associated with each
temperature setpoint 176 and particular occupant activities or
activity levels (e.g., described by the occupant activity data 192
collected by the plurality of thermostats 16 at or near the time
associated with the temperature setpoint 176) and/or particular
events or conditions (e.g., described by the values of the one or
more model inputs at or near the time associated with the
temperature setpoint 176).
[0077] Then, in the illustrated embodiment of the process 260, the
processor may construct (block 270) one or more signature-based
temperature models 196 using the correlated data from block 268.
For example, as described above, in certain embodiments, the
processor may construct the signature-based temperature model 196
by generating a machine-learning agent that is trained using the
correlations that the processor determined between each of the
temperature setpoints 176 (e.g., the time and the temperature
associated with each of the temperature setpoints 176) and the
values determined for the occupancy activity data 192 and/or the
values determined for the one or more model inputs 200. In other
embodiments, as discussed above, the processor may generate the
signature-based temperature model 196 in the form of a set of
instructions (e.g., software module) that define signature-based
temperature setpoints having assigned threshold values that are
based on the correlated data from block 268. In still other
embodiments, as discussed above, the processor may construct the
signature-based temperature model 196 in the form of a number of
weighted temperature curves (e.g., one for each measure of the
occupant activity data 192 and each of the one or more model inputs
200) based on the correlated data from block 268.
[0078] It may be appreciated that, in certain embodiments, the
process 260 may include an additional grouping step. For example,
before correlating the data in block 268, in certain embodiments,
the processor (e.g., the one or more processors 182 of the
thermostat service 180) may use at least a portion of the
classification data received with each set of thermostat data 188
to divide the received sets of thermostat data 188 into groups. For
example, in certain embodiments, the processor may use one or more
pieces of classification data 194, such as location of the
structure 10, model of thermostat 16, type of structure 10, type of
HVAC system 20, temperature profile of the structure 10, or
occupant information, to group the received sets of thermostat data
188. It may be appreciated that this additional grouping step may
enable the processor to construct multiple signature-based
temperature models 196 in block 270 (e.g., one for each group of
the received sets of thermostat data 188), wherein each temperature
signature-based temperature model 196 is generated or constructed
based on a particular group of the received sets of thermostat data
188 and, therefore, may be better tuned to the temperature
preferences of that particular group. For example, the processor
may construct may construct more specialized signature-based
temperature model 196 for a particular locations or geographic
regions (e.g., San Francisco Bay Area, East Texas, New York City, a
particular community), for particular types of structures (e.g.,
one-, two-, or three-story residential structures), for particular
types of occupants (e.g., elderly, college-aged, family with kids,
high-income level, mid-level income level), and so forth.
[0079] Accordingly, after completion of the process 240 of FIG. 8
or the process 260 of FIG. 9, at least one signature-based
temperature model 196 may be constructed to be used by the
processor 112 of the thermostat 16 to implement the signature-based
temperature program. In certain embodiments, for example, after
completion of the process 240 of FIG. 8, the processor 112 of the
thermostat 16 may store the locally generated signature-based
temperature model 196 in the memory 114 for later implementation.
By further example, in certain embodiments, after completion of the
process 260 of FIG. 9, one or more signature-based temperature
models 196 may be generated and stored in the one or more memories
184 of the thermostat service 180. For such embodiments, the
processor 112 of the thermostat 16 may contact the thermostat
service 180 to request and receive one of the generated
signature-based temperature models 196. Further, for embodiments in
which the thermostat service 180 may generate several
signature-based temperature models 196 based on different grouping
of received thermostat data 188 (as discussed above), the
thermostat 16 may include one or more pieces of the classification
data 194 in the request for the signature-based temperature model
196, such that the thermostat service 180 may provide the
thermostat 16 with the most appropriate (e.g., most suitable)
signature-based temperature model 196. After receiving the
signature-based temperature model 196, the processor 112 of the
thermostat 16 may store the signature-based temperature model 196
in the memory 114 for later implementation.
[0080] With the foregoing in mind, FIG. 10 illustrates an
embodiment of a process 280 whereby the processor 112 of the
thermostat 16 may implement a signature-based temperature program
according to a signature-based temperature model 196, as opposed to
a temperature setpoint schedule 170 (e.g., a time-based temperature
program) discussed above with respect to FIG. 5. That is, while the
signature-based temperature model 196 may be constructed based on
the temperature preferences gleaned from one or more temperature
setpoint schedules 170, as discussed above, the signature-based
temperature program selects an appropriate temperature to implement
without relying on a time-based temperature setpoint schedule 170.
The illustrated process 280 starts with the processor 112 of the
thermostat 16 beginning (block 282) implementation of the
signature-based temperature program using the generated or a
received signature-based temperature model 196. That is, the
processor 112 may execute a number of instructions stored in the
memory 114, wherein the instructions are configured to output a
temperature setpoint in response to one or more inputs.
[0081] Accordingly, for the process 280 illustrated in FIG. 10, the
processor 112 may next determine values for the one or more inputs
to feed into the signature-based temperature model 196. For
example, the processor 112 may determine (block 284) current values
for the one or more model inputs 200 used or considered by the
signature-based temperature model 196 from one or more data
resources or online services. That is, the processor 112 of the
thermostat 16 may utilize communication circuitry 116 to request
and receive data from an online resource 124 (e.g., a weather or
emergency alert website, a utility provider website, a stock market
data feed) to determine current values for the one or more model
inputs 200. The processor 112 may also determine (block 286)
current occupant activity data 192 from sensors and/or other data
inputs associated with the thermostat 16. That is, the processor
112 of the thermostat 16 may retrieve from the memory 114 current
occupant activity data 192 based on current measurements of one or
more sensors (e.g., sensors 12 and/or 112) and/or one or more data
inputs 110.
[0082] Then, continuing through the process 280 illustrated in FIG.
10, the thermostat 16 may provide the inputs determined in blocks
284 and 286 to signature-based temperature model 196 to determine a
temperature setpoint output. That is, the processor 112 may
determine (block 288) a temperature setpoint from the
signature-based temperature model 196 using the current occupant
activity (e.g., from block 286) and/or the current values for the
one or more model inputs 200 (e.g., from block 284). Subsequently,
the processor 112 of the thermostat 16 may implement (block 290)
the temperature setpoint determined in block 288. As indicated by
the arrow 292, at some point after implementing a temperature
setpoint according to the output of the signature-based temperature
model 196, the processor 112 may repeat the blocks 284, 286, 288,
and 290. In certain embodiments, the processor 112 may repeat the
blocks 284, 286, 288, and 290 at regular intervals (e.g., every 5
min, 15 min, 30 min, 1 hour, 2 hours, etc.) or based on a received
indication that the current occupant activity and/or current values
of the one or more model inputs have changed by a particular amount
(e.g., beyond a threshold value). At some point during the
execution of blocks 284, 286, 288, and 290 the processor 112 may
end (block 294) implementation of the signature-based temperature
program, for example, in response to user input.
[0083] It may be appreciated that, in certain embodiments, during
implementation of the signature-based temperature program, as
illustrated in FIG. 10, the signature-based temperature model 196
may be further tuned and refined as a result of occupant input. For
example, in an embodiment, an occupant may install a new thermostat
16, and desire to utilize a signature-based temperature program
rather than have the thermostat 16 learn the temperature
preferences of the occupant using the learning mode presented in
FIG. 4. As such, in certain embodiments, the occupant may answer a
questionnaire regarding his or her age, sex, race, weight,
location, size of structure, location of the structure, a ranking
of which model inputs 200 tend to have stronger effects on the
occupant's temperature preferences, and so forth. A processor
(e.g., the processor 112 of the thermostat 16 and/or the processor
182 of the thermostat service 180) may receive these answers and
may generate or select a suitable signature-based temperature model
196 based on the occupant's answers, and this signature-based
temperature model 196 may be used by the thermostat 16 to implement
the signature-based temperature program. However, since the
signature-based temperature model 196 is based on correlations and
trends between typical occupant temperature preferences and
historical values of the model inputs 200 and/or historical
occupant activity data, a particular temperature setpoint
implemented by the thermostat 16 in response to current occupant
activity data 188 and/or current values for the model inputs 200
may not be in accordance with the exact temperature preferences of
a particular occupant.
[0084] For example, in an embodiment, the current values of one or
more weather/emergency model inputs 202 may be provided to the
signature-based temperature model 196 as input, and these current
values may be indicative of a coming winter storm. As such, the
output of the signature-based temperature model 196 may indicate
that the structure 10 should be warmed in advance of the coming
storm. However, a particularly warm-natured occupant, for example,
may not desire to maintain a warmer temperature in the structure 10
in advance of the storm, despite the output of the signature-based
temperature model 196. As such, the occupant may interact with the
thermostat 16 to request a cooler temperature than the output of
the signature-based temperature model 196 dictates, which may be
referred to herein as an "exception" to signature-based temperature
program.
[0085] In response to such an exception, the thermostat 16 may, in
certain embodiments, respond by suspending the signature-based
temperature program for a period of time (e.g., 5 min, 10 min, 1
hour, 2 hours) in which the temperature requested by the occupant
(i.e., the exception temperature) is implemented, and then resuming
implementation of the signature-based temperature program
thereafter. In certain embodiments, the thermostat 16 may,
additionally or alternatively, respond by adjusting the
signature-based temperature model 196 so that the exception
temperature becomes the output of the model whenever the values of
the model inputs 200 (e.g., the one or more weather/emergency model
inputs 202) that resulted in the exception are provided as input at
a later time. That is, the signature-based temperature model 196
may be retrained or reprogrammed such that the pattern of values of
the model inputs 200 and/or occupant activity data 192 that
previously resulted in an exception, subsequently results in
implementing the exception temperature. By specific example, in
certain embodiments, the signature-based temperature model 196 may
be adjusted by altering one or more stored functions of the
signature-based temperature model 196 such that the values for the
model inputs 200 that resulted in the exception are instead
correlated with the exception temperature. By further example, in
certain embodiments, processor may adjust the relative weight of
one or more the functions of the signature-based temperature model
196 to provide, as output, the exception temperature. By still
further example, in certain embodiments, the thermostat 16 may
contact the thermostat service 180 to request a new signature-based
temperature model 196, which may be generated or selected based on
the current signature-based temperature model 196 used by the
thermostat 16, the values of the model inputs 200 and/or occupant
activity data 192 that resulted in the exception, the exception
temperature, the exception time, or a combination thereof.
Accordingly, the signature-based temperature model 196 may
gradually become better tuned to the temperature preferences of the
occupant.
[0086] While only certain features and embodiments of the invention
have been illustrated and described, many modifications and changes
may occur to those skilled in the art (e.g., variations in sizes,
dimensions, structures, shapes and proportions of the various
elements, values of parameters (e.g., temperatures, pressures,
etc.), mounting arrangements, use of materials, colors,
orientations, etc.) without materially departing from the novel
teachings and advantages of the subject matter recited in the
claims. The order or sequence of any process or method steps may be
varied or re-sequenced according to alternative embodiments. It is,
therefore, to be understood that the appended claims are intended
to cover all such modifications and changes as fall within the true
spirit of the invention. Furthermore, in an effort to provide a
concise description of the exemplary embodiments, all features of
an actual implementation may not have been described (i.e., those
unrelated to the presently contemplated best mode of carrying out
the invention, or those unrelated to enabling the claimed
invention). It should be appreciated that in the development of any
such actual implementation, as in any engineering or design
project, numerous implementation specific decisions may be made.
Such a development effort might be complex and time consuming, but
would nevertheless be a routine undertaking of design, fabrication,
and manufacture for those of ordinary skill having the benefit of
this disclosure, without undue experimentation.
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