System and method for optimal load and source scheduling in context aware homes

• US 9,874,885 B2
• Filed: 12/12/2011
• Issued: 01/23/2018
• Est. Priority Date: 12/12/2011
• Status: Active Grant

• Current Assignee: Ademco Inc. (Resideo Technologies, Inc.)
• Original Assignee: Honeywell International Inc.
• Inventors: Shetty, Pradeep, Foslien Graber, Wendy, Mangsuli, Purnaprajna R., Kolavennu, Soumitri N., Curtner, Keith L.
• Primary Examiner(s): Fennema, Robert
• Assistant Examiner(s): Patel, Jigneshkumar
• Application Number:
  US13/323,451
• Publication Number:
  US 20130151012A1
• Time in Patent Office: 2,234 Days
• Field of Search: 700296, 700276, 700291, 713300
• CPC Class Codes: G05D 23/1923 using thermal energy, the cost of which varies in function of time
• CPC Class Codes: G05D 23/1924 using thermal energy, the availability of which is aleatory
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2300/20 The dispersed energy generation being of renewable origin
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/0075 for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
• CPC Class Codes: H02J 3/38 Arrangements for parallely feeding a single network by two or more generators, converters or transformers
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: H02J 3/466 Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
• 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: H04L 12/2827 Reporting to a device within the home network; wherein the reception of the information reported automatically triggers the execution of a home appliance functionality
• 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: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/123 the energy generation units being or involving renewable energy sources
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/20 End-user application control systems
• CPC Class Codes: Y04S 20/244 the home appliances being or involving heating ventilating and air conditioning [HVAC] units
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A controller for controlling energy consumption in a home includes a constraints engine to define variables for multiple appliances in the home corresponding to various home modes and persona of an occupant of the home. A modeling engine models multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context. An optimal scheduler receives the multiple paths of energy utilization and generates a schedule as a function of the multiple paths and a selected persona to place the home in a desired state.

Claims

1. A controller for controlling energy consumption in a home, the controller comprising:

a constraints engine to define sets of constraints for multiple appliances in the home corresponding to various home modes, convenience of service, and persona of an occupant of the home;

a modeling engine to model multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context;

an optimal scheduler to receive the multiple paths of energy utilization, to generate a schedule as a function of the multiple paths and a selected persona with corresponding sets of constraints to place the home in a desired state, and to suggest a plurality of load schedules and set points for optimal energy conservation; and

an occupancy module to determine whether or not the home is currently occupied;

wherein the occupancy module infers occupancy from an overall energy utilization signature and indoor air quality sensor data, wherein the optimal scheduler automatically resets at least one thermostat mode of the home and automatically adjusts at least one thermostat setting of the home, in response to the occupancy module determining that the home is not occupied; and

wherein the controller correlates and maps fuzzy data to parameters that are used for scheduling and controlling operation of home loads.

2. The controller of claim 1 wherein the constraints engine comprises variables for each appliance and subsystem under each mode.

3. The controller of claim 2 wherein the modes include a normal mode, a party mode, and a vacation mode.

4. The controller of claim 1 wherein the persona is indicative of a user high level preferences and goals.

5. The controller of claim 4 wherein the user preference and goals include at least one of optimal energy conservation, dollar saving and comfort conditions.

6. The controller of claim 1 wherein the context of the home includes weather conditions, present state, expected states of sources and loads, and time of use price signal.

7. The controller of claim 1 wherein the controller is driven by anomaly detection.

8. The controller of claim 1 wherein the controller is driven by difference between desired state and current context of the home.

9. The controller of claim 1 wherein the modeling engine includes a model of heating ventilation and air conditioning (HVAC) that captures time to complete state operation, energy required for completing this state transition and cost of energy for this transition.

10. The controller of claim 1 wherein the modeling engine includes a look up table for an appliance that describes state, energy consumption, cost of energy, and time to complete an operation.

11. The controller of claim 1, comprising a cost function engine operable to analyze, for all appliances and for all modes of each appliance, a cost of energy at a discrete time interval.

12. The controller of claim 1, wherein the modeling engine models a plurality of different rates and combinations of rates for the multiple appliances in the home, the plurality of different rates and combinations of rates having a plurality of costs of energy.

13. The controller of claim 12, wherein the plurality of different rates and combinations of rates comprises flow rates of a pool pump.

14. A method for controlling energy consumption in a home, the method comprising:

modeling multiple paths of energy utilization of multiple appliances via a specifically programmed computer to place a home in a desired state from a current context, wherein the appliances have variables selected as a function of home modes and home occupant persona;

receiving the multiple paths of energy utilization via the computer;

generating a schedule via the computer for control of the appliances as a function of the multiple paths and a selected persona to place the home in a desired state, wherein the schedule suggests a plurality of load schedules and set points for optimal energy conservation;

inferring occupancy from an overall energy utilization signature and indoor air quality sensor data; and

correlating and mapping fuzzy data to parameters that are used for scheduling and controlling operation of home loads;

wherein at least one thermostat mode of the home is automatically reset and at least one thermostat setting of the home is automatically adjusted, in response to the home being determined not occupied.

15. The method of claim 14 wherein the modes include a normal mode, a party mode, and a vacation mode, and wherein the persona is indicative of a user preferences and goals.

16. The method of claim 14 and further comprising detecting occupancy of the home, and modifying a mode based on an anomaly between the detected occupancy and an expected mode.

17. The method of claim 16 wherein occupancy is inferred from an overall energy utilization signature.

18. The method of claim 14 wherein the context of the home includes weather conditions, present state, expected states of sources and loads, and time of use price signal.

19. The computer readable storage device of claim 14 wherein the method further comprises detecting occupancy of the home, and modifying a mode based on an anomaly between the detected occupancy and an expected mode, wherein occupancy is inferred from an overall energy utilization signature.

20. A computer readable storage device having instructions to cause a computer to execute a method for controlling energy consumption in a home, the method comprising:

modeling multiple paths of energy utilization of multiple appliances via a specifically programmed computer to place a home in a desired state from a current context, wherein the appliances have variables selected as a function of home modes and home occupant persona;

receiving the multiple paths of energy utilization via the computer;

generating a schedule via the computer for control of the appliances as a function of the multiple paths and a selected persona to place the home in a desired state, wherein the schedule suggests a plurality of load schedules and set points for optimal energy conservation;

inferring occupancy from an overall energy utilization signature and indoor air quality sensor data; and

correlating and mapping fuzzy data to parameters that are used for scheduling and controlling operation of home loads;

wherein at least one thermostat mode of the home is automatically reset and at least one thermostat setting of the home is automatically adjusted, in response to the home being determined not occupied.