Devices, methods, and systems for occupancy detection

• US 10,174,962 B2
• Filed: 10/09/2015
• Issued: 01/08/2019
• Est. Priority Date: 07/27/2011
• Status: Active Grant

• Current Assignee: Ademco Inc. (Resideo Technologies, Inc.)
• Original Assignee: Honeywell International Inc.
• Inventors: Shetty, Pradeep, Foslien, Wendy, Curtner, Keith, Mangsuli, Prunaprajna R., Kolavennu, Soumitri
• Primary Examiner(s): Lau, Tung S
• Application Number:
  US14/879,870
• Publication Number:
  US 20160033157A1
• Time in Patent Office: 1,187 Days
• Field of Search: None
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/56 Remote control
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 2120/10 Occupancy
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: G01D 2204/14 Displaying of utility usage with respect to time, e.g. for monitoring evolution of usage or with respect to weather conditions
• CPC Class Codes: G01D 4/002 Remote reading of utility meters
• CPC Class Codes: G01R 21/133 by using digital technique
• CPC Class Codes: G05B 15/02 electric
• 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: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y04S 20/244 the home appliances being or involving heating ventilating and air conditioning [HVAC] units
• CPC Class Codes: Y04S 20/30 Smart metering, e.g. specially adapted for remote reading

Abstract

Devices, methods, and systems for occupancy detection are described herein. One device embodiment includes a memory and a processor coupled to the memory. The processor is configured to execute executable instructions stored in the memory to determine energy consumption data associated with a structure at a point in time, and determine whether the structure is occupied at the point in time based, at least in part, on the determined energy consumption data.

Claims

1. An HVAC controller for controlling an HVAC system of a structure, the HVAC controller comprising:

a memory storing executable instructions that, when executed, compare energy consumption data to one or more historical energy consumption thresholds to determine an occupancy status of the structure, and when the occupancy status is indicated as being occupied, adjust an HVAC setting to a comfort value, and when the occupancy status is indicated as being unoccupied, adjust the HVAC setting to an energy savings value;

a controller coupled to the memory, wherein the controller is configured to execute the executable instructions stored in the memory to;

obtain energy consumption data associated with the structure;

determine the occupancy status of the structure by comparing the energy consumption data to the one or more historical energy consumption thresholds stored in the memory;

when the determined occupancy status is indicated as being occupied, adjust the HVAC setting of the HVAC system servicing the structure to the comfort value;

when the determined occupancy status is indicated as being unoccupied, adjust the HVAC setting of the HVAC system servicing the structure to the energy savings value; and

thermostatically control the HVAC system servicing the structure in accordance with the comfort value when the adjusted HVAC setting is set to the comfort value, and thermostatically control the HVAC system in accordance with the energy savings value when the adjusted HVAC setting is set to the energy savings setting.

2. The HVAC controller of claim 1, wherein the HVAC setting comprises a heating setting.

3. The HVAC controller of claim 1, wherein the HVAC setting comprises a cooling setting.

4. The HVAC controller of claim 1, wherein the controller is further configured to report the occupancy status to a security system that services the building.

5. The HVAC controller of claim 1, wherein the controller is further configured to report the occupancy status to another device.

6. The HVAC controller of claim 1, wherein the controller is further configured to report the occupancy status to a user device via an electronic message.

7. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on feedback received from a user.

8. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on GPS data associated with a user.

9. The HVAC controller of claim 1, wherein the GPS data associated with the user comprises:

a GPS location of the user; and

a predicted return time of the user to the structure determined based at least in part on the GPS location of the user.

10. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on a particular day of a week.

11. The HVAC controller of claim 1, wherein the controller is configured to determine the occupancy status of the structure based, at least in part, on a particular season of a year.

12. The HVAC controller of claim 1, wherein the comparison of the energy consumption data to the one or more historical energy consumption thresholds comprises comparing a first portion of the energy consumption data to a historical threshold for the first portion of the energy consumption data, and when the first portion of the energy consumption data meets or exceeds the historical threshold for the first portion of the energy consumption data, comparing a second portion of the energy consumption data to a historical threshold for the second portion of the energy consumption data.

13. The HVAC controller of claim 6, wherein the electronic message is one or more of an email message, a text message, a voice message, and a social media message.

14. The HVAC controller of claim 12, wherein:

the first portion of the energy consumption data comprises one or more of a peak current, an RMS current, a rate of change of current, and a frequency of power level changes; and

the historical threshold for the first portion comprises one or more of a corresponding historical peak current, a historical RMS current, a historical rate of change of current, and a historical frequency of power level changes.

15. The HVAC controller of claim 12, wherein the controller is configured to determine when the first portion of the energy consumption data meets or exceeds the historical threshold for the first portion of the energy consumption data via one or more of:

(1) short-time-Fourier transform (STFT)-based spectrum estimation to determine a sinusoidal frequency and a phase content of local sections of the energy consumption data as it changes over a time period;

(2) a non-parametric algorithm;

(3) an adaptive filtering algorithm;

(4) a Kalman filter;

(5) a wavelet based time-frequency estimation algorithm; and

(6) a wavelet transform based algorithm.

16. The HVAC controller of claim 12, wherein the controller is configured to determine when the second portion of the energy consumption data meets or exceeds the historical threshold for the second portion of the energy consumption data using one or more of:

(1) short-time-Fourier transform (STFT)-based spectrum estimation to determine a sinusoidal frequency and a phase content of local sections of the energy consumption data as it changes over a time period;

(2) a non-parametric algorithm;

(3) an adaptive filtering algorithm;

(4) a Kalman filter;

(5) a wavelet based time-frequency estimation algorithm; and

(6) a wavelet transform based algorithm.