U.S. patent application number 14/103270 was filed with the patent office on 2015-06-11 for intelligent thermostat control system.
This patent application is currently assigned to International Business Machines Corporation. The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Michael S. Daubman, Jessica P. Doherty, Jenny S. Li, Maura K. Schoonmaker, Tina M. Tarquinio.
Application Number | 20150159893 14/103270 |
Document ID | / |
Family ID | 53270771 |
Filed Date | 2015-06-11 |
United States Patent
Application |
20150159893 |
Kind Code |
A1 |
Daubman; Michael S. ; et
al. |
June 11, 2015 |
INTELLIGENT THERMOSTAT CONTROL SYSTEM
Abstract
An intelligent thermostat control system for a building, such as
a residential home, that automatically adjusts a thermostat setting
in the home based on real-time data continually received from
mobile devices and/or social media files associated with the
residents. This allows the thermostat controller to override the
explicit programmed settings with implicit settings based on
activity analysis taking the actual locations and schedules of the
residents into account. The intelligent thermostat controller may
control different zones differently to take into account the
schedules and locations of specific residents associated with
specific zones. The temperature controller may also adaptively
learn a number of parameters based on monitored data, such as
travel times and heating/cooling times for the zones based, to
determine times for adjusting the thermostats.
Inventors: |
Daubman; Michael S.;
(Poughkeepsie, NY) ; Doherty; Jessica P.;
(Poughkeepsie, NY) ; Li; Jenny S.; (Danbury,
CT) ; Schoonmaker; Maura K.; (Highland, NY) ;
Tarquinio; Tina M.; (Poughkeepsie, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Assignee: |
International Business Machines
Corporation
Armonk
NY
|
Family ID: |
53270771 |
Appl. No.: |
14/103270 |
Filed: |
December 11, 2013 |
Current U.S.
Class: |
700/278 |
Current CPC
Class: |
F24F 2120/14 20180101;
F24F 11/65 20180101; G05B 13/0265 20130101; G05B 15/02 20130101;
H04L 12/2823 20130101; F24F 2120/10 20180101; F24F 2120/12
20180101; G05B 2219/2614 20130101; H04L 67/12 20130101; F24F 11/62
20180101; F24F 11/70 20180101; F24F 11/64 20180101; H04W 4/029
20180201; F24F 11/56 20180101; H04L 12/2816 20130101; G05D 23/1905
20130101; F24F 11/30 20180101; G05B 2219/2642 20130101; F24F 11/58
20180101; H04W 4/33 20180201; H04M 1/72533 20130101; F24F 2110/00
20180101 |
International
Class: |
F24F 11/00 20060101
F24F011/00; H04L 29/08 20060101 H04L029/08; G05B 15/02 20060101
G05B015/02 |
Claims
1. A thermostat control system, comprising: an intelligent
thermostat controller associated with a building operative to
adjust at least one thermostat within the building; and a
thermostat control application associated with a mobile unit
configured to communicate location information to the thermostat
controller indicating a location of the mobile unit; wherein the
thermostat controller is operative to adjust the thermostat based
on the location information received from the mobile unit.
2. The thermostat control system of claim 1, wherein the thermostat
controller is further operative to adjust the thermostat based on
activity analysis including a predicted travel time from the
location of the mobile unit to the building.
3. The thermostat control system of claim 1, wherein the thermostat
controller is further operative to adjust the thermostat based on
an ambient temperature and a predicted heating/cooling response
time for the building.
4. The thermostat control system of claim 1, wherein the thermostat
controller is further operative to: receive location information
indicating locations of multiple mobile units; perform activity
analysis for multiple residents associated with the mobile units
based on the locations of the mobile units; determine an implicit
thermostat setting based on the activity analysis for the multiple
residents; and adjust the thermostat to the implicit thermostat
setting.
5. The thermostat control system of claim 1, wherein the thermostat
controller is further operative to: adjust multiple thermostats
within the building, wherein each thermostat is associated with a
different resident associated with a respective mobile unit;
determine an implicit thermostat setting for each zone based on
activity analysis for one or more residents associated with the
zone; and adjust the thermostat for each zone to the implicit
thermostat setting for the zone.
6. The thermostat control system of claim 1, wherein the thermostat
controller is further operative to: receive a direct thermostat
setting; adjust the thermostat setting to the direct thermostat
setting; hold the thermostat setting at the direct thermostat
setting for a hold period; conduct activity analysis to determine
an implicit thermostat setting based on activity analysis after the
hold period; and adjust the thermostat setting to the implicit
thermostat setting determined after the hold period.
7. The thermostat control system of claim 1, wherein the thermostat
controller is further operative to: adaptively learn a predicted
travel time from a location of the mobile unit to the building
based on experience monitoring travel times; and adjust the
thermostat based on the location information and the adaptively
learned travel time.
8. The thermostat control system of claim 1, wherein the thermostat
controller is further operative to: adaptively learn a predicted
heating/cooling time for the building based on experience
monitoring heating/cooling times; and adjust the thermostat based
on the adaptively learned heating/cooling time.
9. An intelligent thermostat control system, comprising: a
thermostat controller associated with a building operative to
adjust at least one thermostat within the building; wherein the
thermostat controller is operative to receive schedule information
from a social media file associated with a resident of the
building; and wherein the thermostat controller is operative to
adjust the thermostat based on the schedule information received
from the social media file.
10. The thermostat control system of claim 9, wherein the
thermostat controller is further operative to adjust the thermostat
based on activity analysis including a predicted travel time based
on the schedule information.
11. The thermostat control system of claim 9, wherein the
thermostat controller is further operative to adjust the thermostat
based on a predicted heating/cooling response time for the
building.
12. The thermostat control system of claim 9, wherein the
thermostat controller is further operative to: receive schedule
information associated with multiple residents; perform activity
analysis for the multiple residents based on the schedule
information for the multiple residents; determine an implicit
thermostat setting based on the activity analysis for the multiple
residents; and adjust the thermostat to the implicit thermostat
setting.
13. The thermostat control system of claim 9, wherein the
thermostat controller is further operative to: adjust multiple
thermostats associated with different zones within the building,
wherein each thermostat is associated one or more residents;
determine an implicit thermostat setting for each zone based on
activity analysis for the residents associated with the zone; and
adjust the thermostat for each zone to the implicit thermostat
setting for the zone.
14. The thermostat control system of claim 9, wherein the
thermostat controller is further operative to: receive a direct
thermostat setting for a zone; adjust the thermostat setting for
the zone to the direct thermostat setting; hold the thermostat
setting at the direct thermostat setting for a hold period; conduct
activity analysis to determine an implicit thermostat setting based
on activity analysis after the hold period; and adjust the
thermostat setting to the implicit thermostat setting determined
after the hold period.
15. The thermostat control system of claim 9, wherein the
thermostat controller is further operative to: adaptively learn a
predicted travel time from a scheduled activity to the building
based on experience monitoring travel times; and adjust the
thermostat based on the schedule information and the adaptively
learned travel time.
16. The thermostat control system of claim 9, wherein the
thermostat controller is further operative to: adaptively learn a
predicted heating/cooling time for the building based on experience
monitoring heating/cooling times; and adjust the thermostat based
on the adaptively learned heating/cooling time.
17. A method for operating an intelligent thermostat control system
for a building, comprising: receiving location information
associated with a resident of the building; receiving schedule
information associated with the resident; performing activity
analysis to determine an implicit thermostat setting based on the
location information and the schedule information; and adjusting a
thermostat setting in the building to the implicit thermostat
setting.
18. The method of claim 17, further comprising: receiving location
information associated with multiple residents of the building;
receiving schedule information associated with the residents;
performing activity analysis to determine an implicit thermostat
setting based on the location information and the schedule
information for the multiple residents; and adjusting a thermostat
setting in the building to the implicit thermostat setting.
19. The method of claim 18, further comprising: associating
different residents with different zones of the building, wherein
each zone has an associated thermostat setting; determining an
implicit thermostat setting for each zone based on the location and
schedule data for one or more residents associated with the zone;
and adjusting a thermostat setting for each zone to an implicit
thermostat setting determined for the respective zone.
20. The method of claim 17, further comprising: adaptively learning
a travel time and a heating/cooling time for the building based on
monitored parameters; and adjusting the thermostat settings to the
implicit setting based on the adaptively learned travel and
heating/cooling times.
Description
TECHNICAL FIELD
[0001] The present invention relates to thermostats for buildings
and, more particularly, to intelligent thermostat controllers.
BACKGROUND
[0002] Many homes have programmable thermostats that allow a user
to enter multiple settings for running the heat, ventilation and
air conditioning (HVAC) systems to match the expected occupancy
patterns of the residents to save energy. Most homes also have
multiple zones with dedicated HVAC units, each having a separate
programmable thermostat. It is generally appropriate to heat or
cool different zones on different schedules to save energy. In some
homes, for example, it may be appropriate to heat and cool the main
living zone during waking hours, while the bedroom zone can be
heated and cooled during sleeping hours. It may also be
advantageous to turn the thermostats well down (i.e., turn the
heating temperature setting up and the air conditioning temperature
down) whenever the residents are scheduled to be away from home for
extended periods, for example during weekday working hours. The
thermostats can be programmed to automatically adjust to more
comfortable settings when the residents are scheduled to return
home. There may also be special events, such as vacations, when the
entire family is away from home for extended periods. When the
entire family is away from the home, energy can be saved by turning
the thermostats well down for the duration of their absence.
[0003] In general, more accurate thermostat programming reflecting
the actual occupancy of the residents can save energy, but few
homeowners remember to adjust the thermostat as frequently as
schedules change. And even if a homeowner were to endeavor to set
the thermostats daily to reflect the expected occupancy schedules
of the residents, the thermostat settings might still turn out to
be less then optimal, at least on some occasions, due to unexpected
changes in the schedules.
SUMMARY
[0004] According to one embodiment of the present invention, an
intelligent thermostat controller for a building receives location
information associated with a resident of the building. The
temperature controller performs activity analysis to determine an
implicit thermostat setting based on the location information and
adjusts a thermostat setting in the building to the implicit
thermostat setting. Alternatively or in addition, the intelligent
thermostat controller may receive schedule information associated
with the resident. The activity analysis may then determine the
implicit thermostat setting based on the schedule information or a
combination of schedule and location information. The implicit
setting may be overridden by a direct thermostat setting, as
desired.
[0005] According to one aspect of an embodiment invention, the
intelligent thermostat controller receives location and/or schedule
information for multiple residents of the building. Different
residents may be associated with different zones that each have
separate thermostat settings. The thermostat controller performs
the activity analysis for multiple residents and multiple zones to
determine the implicit thermostat settings for the different zones
and adjusts the thermostat settings for the zones accordingly.
[0006] According to another aspect of an embodiment invention, the
intelligent thermostat controller adaptively learns parameters,
such as travel times and heating/cooling times for the zones of the
building, based on monitored data received over time. The timing
for adjusting to the implicit thermostat settings is then set based
on the location and/or schedule data for the residents together
with the learned parameters.
[0007] Additional features and advantages are realized through the
techniques of the present invention. Other embodiments and aspects
of the invention are described in detail herein and are considered
a part of the claimed invention. For a better understanding of the
invention with the advantages and the features, refer to the
description and to the drawings accompanying figures.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0008] The subject matter which is regarded as the invention is
particularly pointed out and distinctly claimed in the claims at
the conclusion of the specification. The forgoing and other
features, and advantages of the invention are apparent from the
following detailed description taken in conjunction with the
accompanying drawings in which:
[0009] FIG. 1 is a block diagram for an intelligent thermostat
control system.
[0010] FIG. 2 is a block diagram for a thermostat controller for
the intelligent thermostat control system.
[0011] FIG. 3 is a logic flow diagram for a computer-implemented
routine for operating the thermostat controller for the intelligent
thermostat control system.
DETAILED DESCRIPTION
[0012] Embodiments of the present invention may be realized in an
intelligent thermostat control system for a building, which is
described as a residential home in the examples described below.
The home may have multiple residents and multiple independently
controlled heating, ventilation and air conditioning (HVAC) zones
with separate programmable thermostats. Although many homes have
multiple zones, embodiments of the invention may be applied to
single zone HVAC systems if desired. In addition, while the
embodiments described below are directed to a residential home, the
same principles may be applied to commercial, industrial and any
other temperature controlled location.
[0013] With reference now to FIG. 1, an intelligent thermostat
control system 10 includes a thermostat controller 24 forming part
of the customer premises equipment 20A in a building. In this
example, the building is a residential home that serves to
represent one of many homes and other buildings that may have
similar customer premises equipment 20A-20N. The thermostat
controller may be configured with "contact profiles" containing
network and contact information for downloading the location and
schedule information for each resident. In general, sources of
location and schedule information include mobile units (e.g.,
cellular telephones, tablets, etc.) and social media files (e.g.,
Facebook.RTM., Twitter.RTM., LinkedIn.RTM., etc.) associated with
the residents. In this particular example, the thermostat
controller 24 utilizes the Internet 12 to functionally connect with
online social media files 14A-14N associated with the residents of
the home.
[0014] A social media "location tracking" option typically allows a
user's social media file to track the location of the associated
resident's mobile unit, which allows location data as well as
schedule data to be obtained from the social media file.
Alternatively or in addition, the thermostat controller 24 may
utilize a mobile data network 16 to connect directly with the
mobile units 30A-30N associated with multiple residents of the
home. The mobile units typically maintain location data for the
unit and may also maintain schedule data, such as calendars and
alarms. Some users may prefer to keep their schedule in their
mobile unit rather than a social media file. For these users, the
thermostat controller 24 utilizes a mobile data network 16 to
communicate directly with the user's mobile unit.
[0015] The customer premises equipment 20A includes the thermostats
22 for one or more HVAC zones and the thermostat controller 24,
which may be connected to a local home-area network 26. The
thermostat controller 24 intelligently adjusts the programmed
settings of the thermostats 22 based on monitored schedule and
location data for multiple residents obtained from the mobile
devices and social media files associated with the residents. To do
so, the thermostat controller 24 communicates over one or more
networks, such as the Internet 12 and the wireless data network 16,
with online social media files 14A-14N and mobile units 30A-30N
associated with the residents. In some cases, the social media
files 14A-14N may include location and schedule information for the
residents entered by the residents or communicated from the mobile
units to the social media files. In other cases, the mobile units
may be the best resource for obtaining the schedule and location
data. Depending on the preference of the specific users, the
thermostat controller 24 may access location and schedule
information from mobile units, social media files, or both, as
appropriate for different users. The thermostat controller 24 may
also access these resources in a priority order. For example, the
thermostat controller may be configured to receive data
periodically from a user's mobile unit, and resort to accessing the
user's social media whenever the mobile unit does not report at the
expected intervals.
[0016] Referring to the mobile unit 30A to provide an illustrative
example, alternatively or in addition to accessing a resident's
social media files, the temperature controller 24 may directly
access the resident's mobile unit. To facilitate this connectivity,
this mobile unit includes a thermostat control application or "app"
32 configured to autonomously communicate with the thermostat
controller 24. The app periodically reports location and schedule
data to keep the thermostat controller 24 apprised of the current
location and schedule information maintained on the mobile unit.
The mobile unit typically includes programs with schedule
information, such as a calendar, alarm, or a mail program with
scheduling functionality. The mobile unit may also include a social
media interface 36 that provides location information, schedule
information or both to the resident's social media file.
[0017] FIG. 2 is a block diagram of an example thermostat
controller 24, which typically includes a mobile unit interface 40
configured to communicate via a mobile data network with the
thermostat control app 32 on each resident's mobile unit. The
mobile data interface may implement a "pull" model in which the
mobile unit interface 40 requests data downloads from the app
according to a schedule set by the mobile unit interface.
Alternatively, the mobile data interface for the thermostat
controller 24 may implement a "push" model in which the app
automatically sends data according to a schedule set by the app.
The push model may be advantageous, for example, by allowing the
mobile unit to initiate or increase the frequency of data updates
when the underlying schedule or location data experiences a change.
For example, a schedule or alarm change may cause a data update,
and the frequency of data updates may increase when the location of
the mobile unit is changing. The push model may also reduce the
frequency of data updates in response to low battery and low signal
strength conditions at the mobile unit. In either case, the mobile
unit interface 40 at the thermostat controller 24 continually
receives schedule and location updates from the thermostat control
app running on the mobile unit 32.
[0018] The thermostat controller 24 may also include an online
social media interface 41 that communicates via the Internet with
each resident's online social media file 14. This may provide a
backup, or in some cases a less expensive communication interface,
for users who maintain schedule and location data in their social
media files. A thermostat interface 42 controls the thermostat 22,
which may represent multiple thermostats controlling separate
thermostats for different zones in the building. Although any type
of connection may be utilized, a wireless bridge may be suitable
for connecting the thermostat interface 42 with the thermostat 22.
A local network interface 43 may also be provided to integrate the
thermostat controller with a home area network or other local
network present in the building, which may provide connectivity to
the thermostat 22.
[0019] The thermostat controller 24 also includes a user interface
44, which may typically be accessed from a laptop computer, mobile
device or other remote computer allowing a user to conveniently
program and obtain information from the thermostat controller. For
example, the user interface 44 may be used to create and activate
explicit setting profiles for programming the thermostats in the
building, associate particular residents with specific zones, and
create mobile unit and social media contact information profiles
for the residents. The user interface 44 may also be used to set
various parameters utilized by the thermostat controller, such as
thermostat setting parameters and network interface parameters. It
may also be used to activate mobile devices for use with the
system, activate social media files for use with the system, and so
forth.
[0020] A variety of parameters may be provided for administrative
control by an authorized user, such as maximum heating
temperatures, minimum cooling temperatures, override and interrupt
hold periods, contact information for sending alerts to a system
administrator, network interface parameters, parameters used in
activity analysis, and the like. The thermostat controller may also
record and create historical data files for a variety of
parameters, such as explicit thermostat setting data, implicit
thermostat setting data, direct thermostat setting data, resident
location data, resident schedule data, resident occupancy data,
zone occupancy data, mobile device information reporting data,
social media information reporting data, and so forth.
[0021] The thermostat controller 24 also includes an activity
analysis feature 45, which determines when to replace the explicit
thermostat settings with implicit settings based on mobile unit
locations, schedule data, travel patterns, predicted travel times,
and predicted heating/cooling times required to bring HVAC zones to
desired temperatures. The thermostat controller 24 may utilize
ambient and building temperatures 46 to predict the heating/cooling
times required to bring HVAC zones to desired temperatures.
Activity analysis may also adjust the thermostat settings based on
location data, schedule data, or a combination of location and
schedule data. In particular, activity analysis may use location
data, schedule data, and learned parameters such as predicted
travel times and temperature response data for the HVAC zones to
adjust the thermostat settings sufficiently in advance of the
projected arrival of the resident so that the appropriate zone
temperatures have reached comfortable settings when the resident
arrives at the home.
[0022] While many thermostat adjustment triggers may be defined,
the combination of a resident's schedule indicating that a
scheduled activity has ended together with location information
indicating that the resident is traveling in a manner consistent
with returning home will ordinarily trigger heating or cooling one
or more zones of the residence in time for their return. For
example, the activity analysis my prevent a thermostat from
adjusting from an energy saving setting to a comfortable setting
when the activity analysis determines that the resident is at a
restaurant located 30 minutes away from the home. Once the activity
analysis determines that the resident is headed home, the
thermostat may be adjusted to the comfortable setting about 15
minutes before the resident arrived. An unscheduled return to the
residence may also trigger heating or cooling the residence as a
resident approaches or arrives at the residence. Learned parameters
regarding resident activity, travel times, and zone heating/cooling
response may also be factored into the timing of the adjustments to
the implicit thermostat settings.
[0023] The activities analysis feature 45 may work in conjunction
with a central management feature 47, which keeps track of the
explicit thermostat settings for the various zones and combines the
activity analyses for multiple residents to determine implicit
settings for the thermostats in the various zones. Centralized
management allows the temperature controller to take into account
the activity analyses for multiple residents and the association of
different residents with different HVAC zones in the residence.
Over time, the activities analysis and central management features
may adapt to reflect learned parameters gained by monitoring the
schedules, travel times and heating/cooling response of the various
zones in the building over time. The system may also adapt to
learned behaviors of the residents, such as recurring travel and
activity patterns not reflected in the configured schedule data.
This allows the activity analysis and central management features
to adapt the thermostat control procedures to learned parameters in
an ongoing process to proactively adjust the thermostat settings to
provide comfortable living conditions while minimizing energy
usage.
[0024] Of courts, the activity of the residents will sometimes vary
unpredictably from their planned schedules, residents may forget or
misplace their mobile units, residents may not program all of their
planned activities into their mobile units or social media files,
and other events may occur that unexpectedly vary the occupancy
pattern of the residence. To account for these types of situations,
the thermostat controller allows interrupt settings, such as those
entered directly into a thermostat, to override the explicit
(preprogramed) settings as well as the implicit settings determined
by the thermostat controller through activities analysis. The
thermostat controller therefore operates according to predefined
explicit settings (i.e., the conventional programmed settings
entered into the programmable thermostat) that can be overridden by
implicit settings determined through activity analysis for the
residents based on the location and schedule information received
from their mobile devices and social media. The explicit settings
as well as the implicit may also be overridden by interrupt
settings directly entered into the thermostats. In this manner, the
intelligent thermostat controllers more efficiently meet the needs
of the residents by automatically adjusting thermostat settings
based on monitored locations, activities, and schedules of the
residents, while still allowing the residents to interrupt the
programming through manual thermostat adjustment when needed.
[0025] FIG. 3 is a logic flow diagram for an example routine 60 for
operating the thermostat controller 24 for a representative
thermostat. It will be understood that multiple thermostats may be
controlled in a similar manner for multiple HVAC zones in the
building. In block 62, the controller initially sets the thermostat
to the explicit setting or maintains the current setting in the
absence of a determination to alter or override the current
setting. In the absence of an interrupt at block 67, the routine
advances to block 63, where the controller gathers relevant data,
typically including information obtained from the user interface
(e.g., explicit thermostat settings that may change from time to
time), information obtained from the mobile units (e.g., location
information for the residents), and information obtained from
social media (e.g., scheduled activity data for the residents). The
routine 60 proceeds from block 63 to decision block 64, in which
the controller determines whether the gathered information
indicates that a thermostat change may be indicated. For example,
the gathered data may indicate that one of the residents has varied
from a predefined schedule or is traveling toward the residence. If
the gathered data does not indicate a potential thermostat change,
the "no" branch is followed from decision block 64 to block 62, in
which the thermostat maintains the current thermostat setting,
which may be an explicit setting, an implicit setting or an
interrupt setting.
[0026] If the gathered data indicates a potential thermostat
change, the "yes" branch is followed from decision block 64 to
block 65, in which the thermostat controller runs the activity
analysis to determine a desired thermostat setting typically based
on location and schedule data for one or more residents. When
determining the desired thermostat setting, the thermostat
controller may take into account the explicit setting preset for
the thermostat as well as implicit settings determined for multiple
residents based on activity analysis. For example, the activity
analysis for each resident may include their current location,
their recent locations (e.g., whether they are traveling toward the
residence), and their schedule as determined from their location
and schedule data. The temperature controller may also consider
learned parameters, such as travel times from various locations and
heating/cooling times for the various zones of the building based
on ambient and building temperature measurements. The routine 60
then proceeds from block 65 to block 66, in which the thermostat
controller sets the thermostat to the determined setting typically
via a wireless bridge between the thermostat controller and the
thermostat.
[0027] An interrupt reflecting a directly entered thermostat
setting may be received at any time, as indicated by decision block
67 which follows both block 62 and block 66. An interrupt indicates
that a user has taken an affirmative step to directly set the
temperature, for example by manually adjusting a thermostat to a
desired temperature. If an interrupt has been received, the "yes"
branch is followed from decision block 67 to block 68, in which the
temperature controller sets the thermostat to the direct setting,
which may also use the wireless bridge between the thermostat
controller and the thermostat. The routine 60 then proceeds from
block 68 to block 69, in which the temperature controller waits for
a preset hold period, which may be a user configurable parameter.
After waiting the prescribed time, the routine 60 returns to block
63, in which the temperature controller gathers relevant data. On
the other hand, if an interrupt has not been received at decision
block 67, the "no" branch is followed back to block 63, in which
the temperature controller gathers relevant data, and the routine
continues as previously described.
[0028] While conventional programmable thermostats are typically
capable of being programmed on any desired frequency, most people
program their thermostats once or at most occasionally. A typical
homeowner may initially program the thermostat with seasonal
settings based on expected occupancy of the residence and then fail
to make any further adjustments as schedules change. As a result,
the thermostat programs are often set without attempting to follow
the daily schedules of the residents as those schedules vary over
time. And even if the residents were to program thermostat on a
daily basis, their actual schedules would often vary from their
expected schedules. At present, there is no convenient way for a
homeowner to reprogram the thermostat as schedules change.
Conventional programmable thermostats also lack the ability to take
into account varying scheduled for multiple residents, some of whom
may be associated with specific zones, when establishing the
thermostat settings.
[0029] The intelligent thermostat controller solves this problem by
automatically adjusting the thermostat setting based on real-time
data continually received from mobile devices and social media
files associated with the residents. This allows the thermostat
controller to override the explicit programmed settings with
implicit settings based on activity analysis taking the actual
locations and schedules of the residents into account. The
real-time location and schedule data may be obtained over the
Internet or via a mobile data network from the residents' mobile
devices and social media files. The intelligent thermostat
controller may also control different zones differently to take
into account the schedules and locations of specific users
associated with specific zones. For example, the thermostat setting
for an apartment zone may be adjusted when a person associated with
that zone is determined to be present or in the process of
returning to the home. As another example, the thermostat
controller for a home office or workshop zone may be adjusted when
a specific person associated with that zone is determined to be
present or in the process of returning to the home. It will be
appreciated, of course, that many different thermostat control
schemes may be defined by individual users based on the needs of
their households, which will vary from household to household.
[0030] While mobile devices (e.g., cellular telephones) are
described as the location determining devices carried by the
relevant persons, other types of location or presence determining
devices may utilized. For example, the intelligent thermostat
control system may work with RFID identification cards, entry
control systems, position reporting devices, cameras, automatic
lighting systems, infrared sensors or other type of systems for
detecting the presence of persons within the building. The system
may also use additional types of inputs to implement thermostat
overrides, for example automatically adjusting the thermostat
whenever a specific light switch or other piece of equipment is
turned or off. The embodiments described above provide simple
examples to illustrate the principles of the innovation and many
other options, alternatives and levels of sophistication will
become apparent to those skilled in the art once the basic
principles of the innovation have been ascertained based on the
specific examples provided.
[0031] As an option, the intelligent thermostat control system may
be implemented as part of a home area network, which may provide
connectivity to the thermostats. Whether deployed independently or
as part of a larger computer network, the thermostat control system
may be configured to intelligently control the thermostat settings
for multiple zones of the home based on real-time data indicating
the locations or schedules for multiple residents of the home. The
intelligent thermostat controller may control different zones
differently to take into account the occupancy of specific
residents associated with specific zones. The thermostat controller
may utilize schedule data, location data, or both for each
resident. The location and/or schedule data may be received from
the resident's mobile unit, social media file, or both as desired.
The real-time location and schedule data may be obtained over the
Internet and/or via a mobile data network.
[0032] In addition, the temperature controller may adaptively learn
a number of parameters based on monitored data, such as travel
times based on locations and schedule information for the
residents, and heating/cooling times for the zones based on ambient
and building zone temperatures. For example, the activity analysis
may determine predicted travel times from mobile unit locations to
the home based on the location, time of day and day of the week.
The temperature controller may also adaptively learn activity and
travel patterns for the residents. These adaptively learned
parameters are then used to determine the times for adjusting the
thermostats to the implicit settings. This allows the thermostats
to be set to the implicit settings sufficiently in advance to allow
the zones to reach the desired temperatures in time for the arrival
of the residents.
[0033] To provide one simple example to illustrate the basic
functionality, an implicit setting based on activity analysis may
override a predefined explicit thermostat setting by preventing a
change in the temperature setting until the activity analysis
determines based on location information or schedule information
(or both) that one of the residents is likely on their way home.
Similarly, an implicit setting may override an explicit predefined
setting by adjusting the thermostat setting to a comfortable
setting when the location information indicates that a resident is
enroute home. To continue this example, the person's schedule may
impact the activity analysis when the person leaves the residence.
For example, if that person is scheduled to be at another place,
the thermostat may be immediately adjusted to an energy saving
setting as soon as they leave the premises. But if that person is
scheduled to be home, the temperature controller may maintain the
temperature setting at a comfortable setting for a longer period,
effectively anticipating that they will return home shortly.
Adaptive programming allows the activities analysis to learn
behavior patterns based on the combination of location and schedule
data over time, allowing for more effective thermostat control as
the behavior patterns of the residents are learned over time.
[0034] Activity analysis may also be performed, and combined as
appropriate, for multiple residents who may be associated with
different zones. Schedule data may also be combined with location
data, for example to predict that a resident in transit is not
headed home, but is instead headed to another appointment. In
general, predicted travel times may be based location data alone,
schedule data alone, or a combination of location and schedule
data, typically depending on which data is available for a
particular resident. Predicted HVAC zone temperature response times
may also be taken into consideration, typically based on ambient
temperatures, zone temperatures, and learned heating/cooling
response times to estimate the times required to adjust the zone
temperatures to the desired temperatures. Centralized management
allows the temperature settings to be adjusted based on the
combined needs of the various residents, while the adaptive
programming can learn group behaviors to assist in effective
thermostat control.
[0035] The intelligent thermostat controller achieves advantages
not realized by prior thermostat controllers by intelligently
adjusting the thermostat settings based on real-time monitored
data, which may include both location and schedule data for the
residents. The activities of multiple residents may be considered
since, for example, it may be appropriate to adjust the thermostat
for a common living zone to a comfortable setting whenever any of
the residents are home. In addition, the thermostat settings for
different zones may be varied based on the particular residents
that are determined to be home at any particular time. As another
example, the thermostat setting in an in-law suite, basement
apartment or teenager's room may be controlled on based the
activity analysis for one or more residents assigned to that
particular zone. In yet another example, the thermostat setting in
a workshop or home office may be adjusted to a comfortable setting
only when a specific resident authorized to use the workshop or
office is determined to be present. Similar controls may be defined
for exercise rooms, art studios, or other any other special purpose
zone within the home. Interrupts for direct thermostat settings may
be enabled or disabled for particular zones, residents, times of
day and so forth.
[0036] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one more other features, integers,
steps, operations, element components, and/or groups thereof.
[0037] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
act for performing the function in combination with other claimed
elements as specifically claimed. The description of the present
invention has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limited to the
invention in the form disclosed. Many modifications and variations
will be apparent to those of ordinary skill in the art without
departing from the scope and spirit of the invention. The
embodiment was chosen and described in order to best explain the
principles of the invention and the practical application, and to
enable others of ordinary skill in the art to understand the
invention for various embodiments with various modifications as are
suited to the particular use contemplated.
[0038] The diagrams depicted herein illustrate just one example.
There may be many variations to these diagrams or the steps (or
operations) described therein without departing from the spirit of
the invention. For instance, the steps may be performed in a
differing order or steps may be added, deleted or modified. All of
these variations are considered a part of the claimed
invention.
[0039] While the preferred embodiment to the invention had been
described, it will be understood that those skilled in the art,
both now and in the future, may make various improvements and
enhancements which fall within the scope of the claims which
follow. These claims should be construed to maintain the proper
protection for the invention first described.
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