CONTROLLING AN HVAC SYSTEM IN ASSOCIATION WITH A DEMAND-RESPONSE EVENT

• US 20140277761A1
• Filed: 04/19/2013
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Nest Labs Incorporated (Alphabet Inc.)
• Inventors: MATSUOKA, YOKY, STEFANSKI, MARK D., MALHOTRA, MARK, MINICH, ALLEN J.
• Granted Patent: US 9,810,442 B2
• Time in Patent Office: Days
• US Class Current: 700/276
• 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 11/62 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 2120/20 Feedback from users
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: G05D 23/1904 variable in time
• CPC Class Codes: G05D 23/1905 associated with tele control

Abstract

Apparatus, systems, methods, and related computer program products for carrying out a demand response (DR) event via an intelligent, network-connected thermostat associated with a structure. The systems disclosed include an energy management system in operation with an intelligent, network-connected thermostat located at a structure. The thermostat is operable to control an HVAC system. Control during a DR event period may be performed based on an optimal control trajectory of the HVAC system, where the control trajectory is optimal in that it minimizes a cost function comprising a combination of a first factor representative of a total energy consumption during the DR event period, a second factor representative of a metric of occupant discomfort, and a third factor representative of deviations of a rate of energy consumption over the DR event period.

Claims

1. A method of carrying out a demand response (DR) event via an intelligent, network-connected thermostat associated with a structure, the method comprising:

identifying a DR event that defines a DR event period;

determining an optimized control trajectory of a heating, ventilation, and air conditioning (HVAC) system to be in effect during the DR event period that minimizes a cost function comprising a combination of;

a first factor representative of a total energy consumption during the DR event period;

a second factor representative of a metric of occupant discomfort; and

a third factor representative of deviations of a rate of energy consumption over the DR event period; and

controlling the HVAC system in accordance with the determined optimized control trajectory.

2. The method of claim 1, further comprising:

determining a setpoint temperature profile over the DR event period, including;

calculating a setpoint temperature profile over the DR event period based on an expected indoor temperature trajectory, and

identifying peaks and troughs of the calculated setpoint temperature profile; and

causing setpoint temperatures defined by the setpoint temperature profile to be displayed to a user of the HVAC system.

3. The method of claim 1, wherein determining an optimized control trajectory includes:

generating a plurality of candidate parameter sets, each parameter set characterizing an associated candidate setpoint schedule for the DR event;

identifying an optimal one of the plurality of candidate parameter set, the optimal parameter set being optimal in that it minimizes the cost function when the associated optimal setpoint schedule is applied as an input to a predictive model-based simulation of the HVAC system.

4. The method of claim 3, wherein each parameter set comprises:

a first parameter indicative of a temperature-wise offset from a temperature setpoint of an original setpoint schedule; and

a second parameter indicative of a slope of a linear sequence of temperature setpoints passing through a point at the temperature-wise offset from the temperature setpoint of the original setpoint schedule,

wherein the temperature-wise offset and the slope of the linear sequence of temperature setpoints are designed to assist in reducing the deviations of the rate of energy consumption over the DR event period.

5. The method of claim 4, wherein each parameter set further comprises a third parameter indicative of a maximum HVAC duty cycle period.

6. The method of claim 4, wherein each parameter set further comprises a fourth parameter indicative of a duration of the pre-DR event period.

7. The method of claim 3, wherein the predictive model-based simulation of the HVAC system continuously updates a model of the HVAC system.

8. An intelligent network-connected thermostat for controlling an operation of an HVAC system in a smart home environment, the thermostat comprising:

HVAC control circuitry operable to actuate one or more elements of the HVAC system;

one or more sensors for measuring characteristics of the smart home environment; and

a processor coupled to the HVAC control circuitry and the one or more sensors and operable to cause the thermostat to perform operations including;

identifying a DR event that defines a DR event period;

determining an optimized control trajectory of an HVAC system to be in effect during the DR event period that minimizes a cost function comprising a combination of;

a first factor representative of a total energy consumption during the DR event period;

a second factor representative of a metric of occupant discomfort; and

a third factor representative of deviations of a rate of energy consumption over the DR event period; and

controlling the HVAC system in accordance with the determined optimized control trajectory.

9. The thermostat of claim 8, wherein the processor is further operable to cause the thermostat to perform operations including:

determining whether the HVAC system should be controlled in accordance with a different control trajectory; and

upon determining that the HVAC system should be controlled in accordance with a different control trajectory;

identifying a new control trajectory; and

controlling the HVAC system in accordance with the new control trajectory.

10. The thermostat of claim 9, wherein determining whether the HVAC system should be controlled in accordance with a different control trajectory is performed periodically during the DR event period.

11. The thermostat of claim 9, wherein determining whether the HVAC system should be controlled in accordance with a different control trajectory includes one or more of:

monitoring an indoor temperature of the structure and comparing the monitored indoor temperature of the structure to a predicted indoor temperature of the structure;

monitoring a state of the HVAC system and comparing the monitored state of the HVAC system to a predicted state of the HVAC system;

monitoring a real-time occupancy of the structure; and

determining whether optimization of a control trajectory fails.

12. The thermostat of claim 9, wherein identifying a subsequent control trajectory includes one of:

determining a newly optimized control trajectory that minimizes the cost function;

determining an original control trajectory; and

determining a default control trajectory.

13. The thermostat of claim 8, wherein the processor is further operable to cause the thermostat to perform operations including:

monitoring an indoor temperature of the structure;

comparing the monitored indoor temperature of the structure to a predicted indoor temperature of the structure; and

upon determining that the monitored indoor temperature is different from the predicted indoor temperature of the structure by at least a certain amount;

determining a default control trajectory; and

controlling the HVAC system in accordance with the default control trajectory.

14. The thermostat of claim 8, wherein the processor is further operable to cause the thermostat to perform operations including:

monitoring a state of the HVAC system;

comparing the monitored state of the HVAC system to a predicted state of the HVAC system; and

upon determining that the monitored indoor temperature is different from the predicted indoor temperature of the structure by at least a certain amount;

determining a newly optimized control trajectory that minimizes the cost function; and

controlling the HVAC system in accordance with the newly optimized control trajectory.

15. A tangible non-transitory computer-readable storage medium including instructions that, when executed by a computer processor, cause the computer processor to perform operations including:

identifying a DR event that defines a DR event period;

determining an optimized control trajectory of a heating, ventilation, and air conditioning (HVAC) system to be in effect during the DR event period that minimizes a cost function comprising a combination of;

a first factor representative of a total energy consumption during the DR event period;

a second factor representative of a metric of occupant discomfort; and

a third factor representative of deviations of a rate of energy consumption over the DR event period; and

controlling the HVAC system in accordance with the determined optimized control trajectory.

16. The storage medium of claim 15, wherein the optimized control trajectory includes:

an optimized schedule of temperature setpoints, or

an optimized HVAC duty cycle schedule.

17. The storage medium of claim 15 further including instructions that, when executed by the computer processor, cause the computer processor to perform additional operations including:

determining an original control trajectory of the HVAC system;

comparing the optimized control trajectory to the original control trajectory; and

controlling the HVAC system in accordance with the original control trajectory upon determining that the optimized control trajectory is substantially similar to the original control trajectory.

18. The storage medium of claim 15 further including instructions that, when executed by the computer processor, cause the computer processor to perform additional operations including:

receiving a user input indicative of a user amenability to DR load shifting; and

weighting one or more of the first factor, second factor, and third factor based on the user input.

19. The storage medium of claim 15 further including instructions that, when executed by the computer processor, cause the computer processor to perform additional operations including:

determining an occupancy probability profile indicating a likelihood of a structure associated with the HVAC system being occupied during the DR event period,

wherein the metric of occupant discomfort is determined at least in part on the occupancy probability profile.

20. The storage medium of claim 15, wherein determining an optimized control trajectory includes identifying an optimal one of the plurality of candidate parameter sets by applying the associated optimal setpoint schedule to a model of the HVAC system, the model being operable to predict an indoor temperature trajectory for a given HVAC control trajectory.