OCCUPANCY PATTERN DETECTION, ESTIMATION AND PREDICTION

• US 20120066168A1
• Filed: 09/14/2010
• Published: 03/15/2012
• Est. Priority Date: 09/14/2010
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Nest Labs Incorporated (Alphabet Inc.)
• Inventors: FADELL, Anthony Michael, ROGERS, Matthew Lee, ROGERS, Kipp Avery, ISHIHARA, Abraham K., BEN-MENAHEM, Shahar, SHARAN, Rangoli
• Granted Patent: US 8,510,255 B2
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: H04L 12/282 based on user interaction within the home receiver circuitry for displaying additional information being controlled by a remote control apparatus H04N21/42204
• CPC Class Codes: H04L 12/2823 Reporting information sensed by appliance or service execution status of appliance services in a home automation network device-related reporting H04L43/065; arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom H04Q9/00
• CPC Class Codes: H04Q 2209/40 using a wireless architecture
• CPC Class Codes: H04Q 2209/60 for transmitting utility meters data, i.e. transmission of data from the reader of the utility meter
• CPC Class Codes: H04Q 9/00 Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
• CPC Class Codes: H05B 47/115 by determining the presence or movement of objects or living beings
• CPC Class Codes: H05B 47/13 by using passive infrared detectors
• CPC Class Codes: Y02B 20/40 Control techniques providing energy savings, e.g. smart controller or presence detection
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y04S 20/20 End-user application control systems

Abstract

Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

Claims

1. A system for predicting occupancy of an enclosure comprising:

a model of occupancy based in part on information of the enclosure and/or the expected occupants of the enclosure;

a sensor adapted to detect occupancy within the enclosure; and

an occupancy predictor adapted and programmed to predict future occupancy of the enclosure based at least in part on the model and the sensor.

2. A system according to claim 1 wherein the model is an a priori stochastic model of human occupancy created prior to installation of the system into the enclosure.

3. A system according to claim 2 wherein the a priori stochastic model is a comfort and spatial occupancy model that includes one or more statistical profiles.

4. A system according to claim 3 wherein the a priori stochastic model includes behavior modeling of activity, itinerary, and/or thermal behavior.

5. A system according to claim 1 wherein the model is based at least in part on the type of the enclosure.

6. A system according to claim 5 wherein the enclosure type is selected from a group consisting of:

workplace, single-family home, apartment, and condominium.

7. A system according to claim 1 wherein the model is based at least in part on geometrical and structural data about the enclosure.

8. A system according to claim 1 wherein the model is based at least in part on an expected type of occupant of the enclosure.

9. A system according to claim 8 wherein the expected occupant type depends on one or more occupant attributes selected from a group consisting of:

age, school enrollment status, marital status, relationships status with other occupants, and retirement status.

10. A system according to claim 8 wherein the expected occupant type is selected from a group consisting of:

preschool children, school-age children, seniors, retirees, working-age adults, non-coupled adults, vacationers, office workers, retail store occupants.

11. A system according to claim 1 wherein the model is based at least in part on the expected number, types and/or relational attributes of the occupants of the enclosure.

12. A system according to claim 1 wherein the model is based at least in part on seasons of the year, days of the week, types of day and/or times of day.

13. A system according to claim 1 wherein the model is based at least in part on the geographic location of the enclosure.

14. A system according to claim 1 wherein the enclosure is a dwelling.

15. A system according to claim 1 wherein the enclosure is a workplace.

16. A system according to claim 1 wherein the sensor is selected from a group consisting of:

motion detector, powerline sensor, network traffic monitor, radio traffic monitor, microphone, infrared sensor, accelerometer, ultrasonic sensor, pressure sensor, smart utility meter, and light sensor.

17. A system according to claim 1 wherein the occupancy sensor one of a plurality of occupancy sensors adapted and positioned to detect occupancy within a plurality of sub-regions of the enclosure.

18. A system according to claim 1 further comprising a second sensor, wherein the occupancy prediction of the enclosure is further based in part on the second sensor.

19. A system according to claim 1 wherein the occupancy prediction of the enclosure is further based in part on user inputted data.

20. A system according to claim 19 wherein the user inputted data includes occupancy information directly inputted by an occupant of the enclosure.

21. A system according to claim 19 wherein the user inputted data includes calendar information.

22. A system according to claim 21 wherein the calendar information includes information of a type selected from a group consisting of:

holidays, seasons, weekdays, and weekends.

23. A system according to claim 1 wherein the occupancy prediction is used in the actuation and/or control of an HVAC system for the enclosure.

24. A system according to claim 1 wherein the occupancy prediction is used in one or more systems of a type selected from a group consisting of:

hot water heating, home automation, home security, lighting management, charging of rechargeable batteries.

25. A system according to claim 1 wherein the occupancy predictor is further adapted and programmed to predict occupancy based in part on detected periodicities in inputted data.

26. A system according to claim 1 wherein the occupancy predictor includes one or more algorithms for predicting occupancy based on one or more occupancy patterns.

27. A system according to claim 1 wherein the occupancy predictions are based in part on a maximum-likelihood approach.

28. A system according to claim 1 wherein the model of occupancy is updated based on comparing output of the occupancy predictor and sensor.

29. A system according to claim 1 wherein the model of occupancy includes one or more types of models selected from a group consisting of:

Bayesian Network, Hidden Markov Model, Hidden Semi-Markov Model, variant of Markov model, and Partially Observable Markov Decision Process.

30. A method for predicting occupancy of an enclosure comprising:

receiving a model of occupancy patterns based in part on information of the enclosure and/or the expected occupants of the enclosure;

receiving occupancy data from a sensor adapted to detect occupancy within the enclosure; and

predicting future occupancy of the enclosure based at least in part on the model and the occupancy data.

31. A method according to claim 30 wherein the method is carried out by a occupancy prediction system, and the model is an a priori stochastic model of human occupancy created prior to installation of the system into the enclosure.

32. A method according to claim 31 wherein the a priori stochastic model is a comfort and spatial occupancy model that includes one or more statistical profiles.

33. A method according to claim 30 wherein the model is based at least in part on the type of the enclosure.

34. A method according to claim 33 wherein the enclosure type is selected from a group consisting of:

workplace, single-family home, apartment, and condominium.

35. A method according to claim 30 wherein the model is based at least in part on geometrical and structural data about the enclosure.

36. A method according to claim 30 wherein the model is based at least in part on an expected type of occupant of the enclosure.

37. A method according to claim 36 wherein the expected occupant type depends on one or more occupant attributes selected from a group consisting of:

age, school enrollment status, marital status, relationships status with other occupants, and retirement status.

38. A method according to claim 36 wherein the expected occupant type is selected from a group consisting of:

preschool children, school-age children, seniors, retirees, working-age adults, non-coupled adults, vacationers, office workers, retail store occupants.

39. A method according to claim 30 wherein the model is based at least in part on seasons of the year, days of the week, types of day and/or times of day.

40. A method according to claim 30 wherein the model is based at least in part on the geographic location of the enclosure.

41. A method according to claim 30 wherein the enclosure is a dwelling.

42. A method according to claim 30 wherein the enclosure is a workplace.

43. A method according to claim 30 wherein the sensor is selected from a group consisting of:

motion detector, powerline sensor, network traffic monitor, radio traffic monitor, microphone, infrared sensor, accelerometer, ultrasonic sensor, pressure sensor, smart utility meter, and light sensor.

44. A method according to claim 30 wherein the occupancy data includes information from a second sensor.

45. A method according to claim 30 wherein the occupancy prediction of the enclosure is further based in part on user inputted data.

46. A method according to claim 45 wherein the user inputted data includes occupancy information directly inputted by an occupant of the enclosure.

47. A method according to claim 45 wherein the user inputted data includes calendar information.

48. A method according to claim 47 wherein the calendar information includes information of a type selected from a group consisting of:

holidays, seasons, weekdays, and weekends.

49. A method according to claim 30 wherein the occupancy prediction is used in the actuation and/or control of an HVAC system for the enclosure.

50. A method according to claim 30 wherein the occupancy prediction is used in one or more systems of a type selected from a group consisting of:

home automation, home security, lighting control, charging of rechargeable batteries.

51. A Method according to claim 30 wherein the occupancy prediction is based in part on detected periodicities in inputted data.

52. A method according to claim 30 wherein the occupancy prediction includes one or more algorithms for predicting occupancy based on one or more occupancy patterns.

53. A method according to claim 30 further comprising updating the model of occupancy patterns based at least in part on comparing predicted future occupancy of the enclosure and occupancy data from the sensor.

54. A method for detecting occupancy of an enclosure comprising:

monitoring communication signals which tend to indicate a likelihood of occupancy of the enclosure; and

estimating occupancy based at least in part on the monitored communication signals.

55. A method according to claim 54 wherein in the communication signals is one or more network connections and the monitoring includes monitoring for changes in network communications which tend to indicate a likelihood of occupancy.

56. A method according to claim 55 wherein the one or more network connections include a network connection of a type selected from a group consisting of:

wi-fi and email.

57. A method according to claim 55 wherein the changes in network communication includes monitoring changes in network traffic which indicate a likelihood of internet usage.

58. A method according to claim 54 wherein the communication signals are radio frequency communication signals.

59. A method according to claim 58 wherein the communication signals are mobile phone signals.

60. A method according to claim 54 wherein the enclosure is a single family dwelling.

61. A method according to claim 54 further comprising predicting future occupancy of the enclosure based in part on the estimated occupancy.

62. A method according to claim 54 further comprising controlling an HVAC system for the enclosure based at least in part on the estimated occupancy.

63. A method according to claim 54 wherein in the communication signals are monitored for patterns which tend to indicate a likelihood of occupancy.

64. A method for detecting occupancy of an enclosure comprising:

monitoring sound pressure information which tend to indicate a likelihood of occupancy of the enclosure; and

estimating occupancy based at least in part on the monitored pressure information.

65. A method according to claim 64 wherein the sound pressure monitored is in the audible range.

66. A method according to claim 65 wherein the types of sounds monitored include one or more types selected from a group consisting of:

footsteps, voices, and doors closing.

67. A method according to claim 64 wherein the sound pressure monitored is in the ultrasonic range.

68. A method according to claim 67 wherein the types of sounds monitored include one or more types selected from a group consisting of footsteps, and air movement due to human movement.

69. A method for detecting occupancy of an enclosure comprising:

monitoring utility information for the enclosure which tends to indicate a likelihood of occupancy of the enclosure; and

estimating occupancy of the enclosure based at least in part on the monitored utility information.

70. A method according to claim 69 wherein the utility information is powerline information, and the powerline information is filtered to detect the use of electronic devices which tend to indicate a likelihood of occupancy in the enclosure.

71. A method according to claim 69 wherein the utility information is obtained from a Smart Meter that tends to indicate a likelihood of occupancy in the enclosure.

72. A method according to claim 71 wherein pattern recognition is used to monitor changes in the utility information from a baseline to detect likelihoods of occupancy of the enclosure.

73. A method according to claim 69 wherein the enclosure is a dwelling.

74. A method for detecting occupancy of an enclosure comprising:

a sensing system adapted to monitor utility information for the enclosure which tends to indicate a likelihood of occupancy of the enclosure; and

a processing system adapted and programmed to estimate occupancy of the enclosure based at least in part on the monitored utility information by the sensing system.

75. A system according to claim 74 wherein the utility information is powerline information, and the powerline information is filtered to detect the use of electronic devices which tend to indicate a likelihood of occupancy in the enclosure.

76. A system according to claim 74 wherein the utility information is obtained from a Smart Meter that tends to indicate a likelihood of occupancy in the enclosure.

77. A method according to claim 76 wherein pattern recognition is used by the processing system to monitor changes in the utility information from a baseline to detect likelihoods of occupancy of the enclosure.

78. A method according to claim 74 wherein the enclosure is a dwelling.

79. A method for detecting occupancy of an enclosure comprising:

monitoring motion information in close proximity to a sensor; and

estimating occupancy based at least in part on the monitored motion information.

80. A method according to claim 79 wherein the sensor is an accelerometer and the motion information is acceleration information monitored within about one meter of the sensor.

81. A method according to claim 79, further comprising preparing a device for interaction with a user based on the monitored motion information.

82. A method according to claim 79 wherein the enclosure is a small dwelling.

83. A method for detecting occupancy of an enclosure comprising:

monitoring infrared signals within the enclosure which tend to indicate operation of one or more infrared controllable devices; and

estimating occupancy of the enclosure based at least in part on the monitored infrared signals.

84. A method according to claim 83 wherein the infrared signals tend to indicate remote control of one or more electronic devices by an occupant.

85. A method according to claim 83 wherein the enclosure is a dwelling.

86. A system for detecting occupancy of an enclosure comprising:

an infrared sensor adapted to monitor infrared signals within the enclosure which tend to indicate operation of one or more infrared controllable devices; and

a processing system adapted and programmed to estimate occupancy of the enclosure based at least in part on the monitored infrared signals by the infrared sensor.

87. A system according to claim 86 wherein the infrared signals tend to indicate remote control of one or more electronic devices by an occupant.

88. A method for detecting occupancy of an enclosure comprising:

monitoring pressure information within the enclosure which tends to indicate occupancy of the enclosure; and

estimating occupancy of the enclosure based at least in part on the monitored pressure information.

89. A method according to claim 88 wherein the pressure information includes change in atmospheric pressure in the enclosure that tend to indicate opening of doors and/or windows.

90. A method according to claim 88 wherein the enclosure is a dwelling.

91. A system for detecting occupancy of an enclosure comprising:

a pressure sensor adapted to monitor pressure information within the enclosure which tends to indicate occupancy of the enclosure; and

a processing system adapted and programmed to estimate occupancy of the enclosure based at least in part on the monitored pressure information by the pressure sensor.

92. A system according to claim 91 wherein the pressure information includes change in atmospheric pressure in the enclosure that tend to indicate opening of doors and/or windows.

93. A method for detecting occupancy of an enclosure comprising:

monitoring ambient light information within the enclosure which tends to indicate occupancy of the enclosure; and

estimating occupancy of the enclosure based at least in part on the monitored ambient light information.

94. A method according to claim 93 wherein the ambient information includes rapid changes in ambient light in the enclosure that tends to indicate switching on and off of lights or sudden opening or closing of blinds and/or curtains.

95. A method according to claim 93 wherein the ambient information includes wavelength composition of incident light that tends to indicate whether the light source is artificial or natural.

96. A method according to claim 93 wherein the enclosure is a dwelling.

97. A system for detecting occupancy of an enclosure comprising:

an ambient light sensor adapted to monitor ambient information within the enclosure which tends to indicate occupancy of the enclosure; and

a processing system adapted and programmed to estimate occupancy of the enclosure based at least in part on the monitored ambient light information by the ambient light sensor.

98. A system according to claim 97 wherein the ambient information includes rapid changes in ambient light in the enclosure that tends to indicate turning on and off of lights or opening and closing of blinds and/or curtains.

99. A system according to claim 97 wherein the ambient information includes whether ambient light is from artificial source that tends to indicate presence of occupants in certain structure types.