System and method for determining occupancy schedule for controlling a thermostat

Patent metadata

Field	Value
Publication number	US-10001792-B1
Application number	US-201414303165-A
Country	US
Kind code	B1
Filing date	Jun 12, 2014
Priority date	Jun 12, 2013
Publication date	Jun 19, 2018
Grant date	Jun 19, 2018

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.

First claim

What is claimed is:

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

Assignees

• Opower Inc

Inventors

• Packer Ben
• Fonts Agustin
• Shilts Erik
• Chung Garner
• Curtis Richard Tyler

Classifications

• G05D23/32 Primary

  with provision for adjustment of the effect of the auxiliary heating device, e.g. a function of time · CPC title

• G05D23/1904 Primary

  variable in time · CPC title

• F24F2120/10

  Occupancy · CPC title

• F24F2140/50

  Load · CPC title

• F24F11/62

  characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values · CPC title