System and method for determining occupancy schedule for controlling a thermostat

• US 10,001,792 B1
• Filed: 06/12/2014
• Issued: 06/19/2018
• Est. Priority Date: 06/12/2013
• Status: Active Grant

• Current Assignee: Opower, Inc. (Oracle Corporation)
• Original Assignee: Opower, Inc. (Oracle Corporation)
• Inventors: Packer, Ben, Fonts, Agustin, Shilts, Erik, Chung, Garner, Curtis, Richard Tyler
• Primary Examiner(s): Von Buhr, M. N.
• Application Number:
  US14/303,165
• Time in Patent Office: 1,468 Days
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• 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 11/65 for selecting an operating mode
• CPC Class Codes: F24F 2120/10 Occupancy
• CPC Class Codes: F24F 2140/50 Load
• CPC Class Codes: G05D 23/1904 variable in time
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

An occupancy schedule determining method and system that receives usage data indicating a quantity of a resource supplied by a utility that is used at the location over a plurality of days, each of the plurality of days being subdivided into a plurality of predetermined time periods, and the usage data indicating the quantity of the resource supplied by the utility that is used during each of the predetermined time periods, aggregates the usage data for each of the plurality of predetermined time periods over the plurality of days, and uses the aggregated usage data to determine the occupancy schedule at the location using a processor.

Claims

1. A method for determining a predicted user schedule at a location wherein the method is performed by a computing system including at least a processor for executing instructions, the method comprising:

receiving, by at least the processor, usage data indicating a quantity of a resource supplied by a utility that is used at the location over a plurality of days;

aggregating, by at least the processor, the usage data for each of a plurality of predetermined time periods subdivided from the plurality of days;

generating, by at least the processor using the aggregated usage data, a load curve that represents variations in usage of the quantity of the resource over the plurality of days;

creating, by at least the processor, a set of load curves based upon customer usage profiles of a plurality of customers, wherein the set of load curves have a set of clustering inputs derived from state changes within the set of load curves;

computing, by at least the processor, predictor inputs of the load curve, wherein the predictor inputs are derived from state changes within the load curve;

comparing, by at least the processor, distances between the predictor inputs derived from the state changes within the load curve to the set of clustering inputs derived from state changes within the set of load curves to identify a target load curve within the set of load curves that closest matches the load curve, wherein the comparing comprises scoring predictor inputs based upon Euclidean distances between the predictor inputs and the set of clustering inputs, wherein the target load curve is identified based upon the target load curve having a score corresponding to a minimum Euclidean distance;

generating, by at least the processor, a predicted user schedule based upon an occupancy schedule assigned to the target load curve; and

controlling, by at least the processor, settings of a thermostat using a heating schedule, a cooling schedule, or a heating and cooling schedule generated using the predicted user schedule.

2. The method of claim 1, further comprising:

determining a time at which a peak event of a demand response event will occur;

responsive to determining that the time corresponds to a time at which the user will not be occupying the location as indicated by the occupancy schedule, determining that the user is a candidate to enroll in the demand response event; and

responsive to determining that the time corresponds to a time at which the user will be occupying the location as indicated by the occupancy schedule, determining that the user is not a candidate to enroll in the demand response event.

3. The method of claim 1, wherein the computing predictor inputs comprise computing the predictor inputs derived from state changes comprising at least one of a wake up state change, a leave home state change, a return home sleep state change, or a go to sleep state change.

4. The method of claim 3, wherein the providing the predicted user schedule at the location to the system controller comprises providing the predicted user schedule at the location to the thermostat that controls a heating system, a cooling system, or a heating and cooling system at the location.

5. The method of claim 1, wherein the predicted user schedule indicates time periods when the location is predicted to be occupied and time periods when the location is predicted to be unoccupied.

6. The method of claim 1, further comprising:

receiving additional usage data indicating a second quantity of the resource supplied by the utility that is used at the location over a plurality of new days, each of the plurality of new days being subdivided into the plurality of predetermined time periods;

aggregating the additional usage data for each of the plurality of predetermined time periods over the plurality of new days; and

using the aggregated additional usage data to update the predicted user schedule at the location.

7. A non-transitory computer readable medium storing a program of instructions that when executed by at least a processor of a computing device causes the processor to:

receive, by the processor, usage data for a resource that is used at a location over a plurality of days;

aggregate, by the processor, the usage data for each of a plurality of predetermined time periods subdivided from the plurality of days;

generate, by at least the processor using the aggregated usage data, a load curve that represents variations in usage of the quantity of the resource over the plurality of days;

create, by at least the processor, a set of load curves based upon customer usage profiles of a plurality of customers, wherein the set of load curves have a set of clustering inputs derived from state changes within the set of load curves;

compute, by at least the processor, predictor inputs of the load curve, wherein the predictor inputs are derived from state changes within the load curve;

compare, by at least the processor, distances between the predictor inputs derived from the state changes within the load curve to the set of clustering inputs derived from state changes within the set of load curves to identify a target load curve within the set of load curves that closest matches the load curve, wherein the comparing comprises scoring predictor inputs based upon Euclidean distances between the predictor inputs and the set of clustering inputs, wherein the target load curve is identified based upon the target load curve having a score corresponding to a minimum Euclidean distance;

generate, by at least the processor, a predicted user schedule based upon an occupancy schedule assigned to the target load curve; and

control, by at least the processor, settings of a thermostat using a heating schedule, a cooling schedule, or a heating and cooling schedule generated using the predicted user schedule.

8. The non-transitory computer readable medium of claim 7, wherein the receiving the usage data comprises receiving data indicating the quantity of at least one of electricity, gas, or water supplied by a utility resource provider to the location over the plurality of days.

9. The non-transitory computer readable medium of claim 7, further comprising instructions that when executed by the processor cause the processor to:

provide the predicted user schedule at the location to a system controller that controls a system at the location.

10. The non-transitory computer readable medium of claim 9, wherein the providing the predicted user schedule at the location to the system controller comprises providing the predicted user schedule at the location to the thermostat that controls a heating system, a cooling system, or a heating and cooling system at the location.

11. The non-transitory computer readable medium of claim 7, wherein the predicted user schedule indicates time periods when the location is predicted to be occupied and time periods when the location is predicted to be unoccupied.

12. The non-transitory computer readable medium of claim 7, further comprising instructions that when executed by the processor cause the processor to:

receive additional usage data indicating a second quantity of the resource that is used at the location over a plurality of new days, each of the plurality of new days being subdivided into the plurality of predetermined time periods;

aggregate the additional usage data for each of the plurality of predetermined time periods over the plurality of new days and generate a second load curve; and

using the second load curve to update the predicted user schedule at the location.

13. A system for determining an occupancy schedule at a location, the system comprising:

at least one processor connected to at least one memory;

a usage data receiver that receives, by network communications, usage data of a resource that is used at the location over a plurality of days;

a usage data aggregator implemented with instructions that when executed by at least the one processor cause the at least one processor to aggregate the usage data for each of a plurality of predetermined time periods subdivided from the plurality of days; and

an occupancy schedule determiner implemented with instructions that when executed by at least the one processor cause the at least one processor to;

generate, using the aggregated usage data, a load curve that represents variations in usage of the quantity of the resource over the plurality of days;

create a set of load curves based upon customer usage profiles of a plurality of customers, wherein the set of load curves have a set of clustering inputs derived from state changes within the set of load curves;

compute predictor inputs of the load curve, wherein the predictor inputs are derived from state changes within the load curve;

compare distances between the predictor inputs derived from the state changes within the load curve to the set of clustering inputs derived from state changes within the set of load curves to identify a target load curve within the set of load curves that closest matches the load curve, wherein the comparing comprises scoring predictor inputs based upon Euclidean distances between the predictor inputs and the set of clustering inputs, wherein the target load curve is identified based upon the target load curve having a score corresponding to a minimum Euclidean distance;

generate a predicted user schedule based upon an occupancy schedule assigned to the target load curve; and

control settings of a thermostat using a heating schedule, a cooling schedule, or a heating and cooling schedule generated using the predicted user schedule.

14. The system of claim 13, wherein the usage data received by the usage data receiver indicates the quantity of at least one of electricity, gas, network bandwidth, or water used by the location over the plurality of days.

15. The system of claim 13, further comprising:

an occupancy schedule provider that transmits, via a network, the predicted user schedule at the location to a system controller that controls a system at the location.

16. The system of claim 15, wherein the system controller to which the occupancy schedule provider provides the predicted user schedule at the location comprises the thermostat that controls a heating system, a cooling system, or a heating and cooling system at the location.

17. The system of claim 13, wherein the predicted user schedule determined by the occupancy schedule determiner indicates time periods when the location is predicted to be occupied and time periods when the location is predicted to be unoccupied.