OCCUPANCY PATTERN DETECTION, ESTIMATION AND PREDICTION

• US 20150006463A1
• Filed: 07/02/2014
• Published: 01/01/2015
• Est. Priority Date: 09/14/2010
• Status: Active Grant

• Current Assignee: Nest Labs Incorporated (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 9,245,229 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 patterns based in part on information regarding the enclosure and/or the expected occupants of the enclosure;

a sensor configured to detect occupancy within the enclosure; and

an occupancy predictor configured to predict future occupancy of the enclosure based at least in part on the model and the occupancy detected by the sensor.

2. The system of claim 1 wherein the model is an a priori stochastic model of human occupancy.

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

4. The system of claim 2 wherein the a priori stochastic model includes behavior modeling of activity, itinerary, and/or thermal behavior.

5. The system of claim 1 wherein the model is based at least in part on information selected from the group consisting of:

a type of the enclosure, geometrical data about the enclosure, structural data about the enclosure, geographic location of the enclosure, an expected type of occupant of the enclosure, an expected number of occupants of the enclosure, the relational attributes of the occupants of the enclosure, seasons of the year, days of the week, types of day, and times of day.

6. The system of 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.

7. The system of claim 1 further comprising a second sensor, wherein the occupancy predictor is configured to predict future occupancy of the enclosure based at least in part on the model, the sensor, and the second sensor.

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

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

receiving occupancy data from a sensor configured to detect occupancy within the enclosure, the occupancy data being indicative of the occupancy detected by the sensor; and

predicting, by a computing device, future occupancy of the enclosure based at least in part on the model and the occupancy data.

9. The method of claim 8 further comprising receiving user inputted data, wherein the future occupancy of the enclosure is predicted further based in part on the user inputted data.

10. The method of claim 9 wherein the user inputted data includes occupancy information directly inputted by an occupant of the enclosure and/or calendar information.

11. The method of claim 9 further comprising detecting periodicities in the user inputted data, wherein the future occupancy of the enclosure is predicted further based in part on the detected periodicities in the user inputted data.

12. The method of claim 8 further comprising:

comparing the predicted future occupancy of the enclosure with the occupancy data from the sensor; and

updating the model of occupancy patterns based at least in part on the result of the comparison.

13. The method of claim 8 wherein the sensor is one of a plurality of sensors arranged at different sub-regions of the enclosure, receiving occupancy data from a sensor includes receiving occupancy data from the plurality of sensors, and predicting future occupancy of the enclosure includes predicting future occupancy of the enclosure based at least in part on the occupancy data received from the plurality of sensors.

14. The method of claim 8 wherein the future occupancy predictions are based at least in part on a maximum-likelihood approach.

15. A tangible non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform operations comprising:

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

receiving occupancy data from a sensor configured to detect occupancy within the enclosure, the occupancy data being indicative of the occupancy detected by the sensor; and

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

16. The storage medium of claim 15 wherein the model is based at least in part on an expected occupant type.

17. The storage medium of claim 16 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.

18. The storage medium of claim 16 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, and retail store occupants.

19. The storage medium of claim 15 wherein the model of occupancy patterns 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.

20. The storage medium of claim 15 wherein the future occupancy prediction is used in one or more systems of a type selected from a group consisting of:

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