Optimizing and controlling the energy consumption of a building

• US 9,471,082 B2
• Filed: 02/05/2016
• Issued: 10/18/2016
• Est. Priority Date: 01/23/2012
• Status: Active Grant

• Current Assignee: Ademco Inc. (Resideo Technologies, Inc.), University of Maryland, College Park
• Original Assignee: Earth Networks Incorporated, University of Maryland, College Park
• Inventors: Marshall, Robert S., Sloop, Christopher Dale, Oberholzer, David, Kim, Jungho, Siemann, Michael
• Primary Examiner(s): Lo, Kenneth M
• Assistant Examiner(s): Wellman, Geoffrey
• Application Number:
  US15/017,499
• Publication Number:
  US 20160154417A1
• Time in Patent Office: 256 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G05B 13/02 electric
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05D 23/1917 using digital means
• CPC Class Codes: G05F 1/66 Regulating electric power

Abstract

Described herein are methods and systems, including computer program products, for optimizing and controlling the energy consumption of a building. A computing device generates thermal response coefficients for the building based on energy characteristics of the building and weather data associated with the location of the building. The computing device an energy response for each of a plurality of different points in time based on the thermal response coefficients and forecasted weather conditions. The computing device selects minimal energy requirements based on an energy consumption cost. The computing device determines a series of temperature set points based on the corresponding energy response and the minimal energy requirements. The computing device transmits the series of temperature set points to a thermostat, which adjusts operating parameters using each series of temperature set points when a time value matches the point in time associated with each series of temperature set points.

Claims

1. A method for optimizing and controlling the energy consumption of a building, the method comprising:

generating, by a first computing device, thermal response coefficients for the building based on energy characteristics of the building and weather data associated with the location of the building, including;

receiving, by the first computing device, an indoor air temperature of the building;

inferring, by the first computing device, physical structure data of the building based upon the location of the building;

determining, by the first computing device, a transient temperature within one or more walls of the building based upon indoor air temperature and the weather data associated with the location of the building; and

adjusting, by the first computing device, the physical structure data based upon the transient temperature; and

generating, by the first computing device, the thermal response coefficients for the building based upon the adjusted physical structure data;

predicting, by the first computing device, an energy response of the building for each of a plurality of different points in time during a day, wherein each energy response is based upon the thermal response coefficients and forecasted weather conditions associated with the location of the building for one of the points in time;

selecting, by the first computing device, minimal energy requirements of the building based on an energy consumption cost associated with the building;

determining, by the first computing device, a series of temperature set points for the building for each of the plurality of different points in time based on the corresponding energy response and the minimal energy requirements;

transmitting, by the first computing device, the series of temperature set points to a thermostat of the building; and

adjusting, by the thermostat, operating parameters of the thermostat using each series of temperature set points when a time value stored in the thermostat matches the point in time associated with each series of temperature set points.

2. The method of claim 1, wherein the physical data comprises at least one of:

thermal mass, wind infiltration, relative area of windows, amount of insulation, material of construction, wind infiltration of the building, and efficiency of an associated HVAC system.

3. The method of claim 1, further comprising ranking, by the first computing device, the predicted energy responses based on predetermined criteria.

4. The method of claim 1, wherein the step of predicting an energy response is further based on the energy consumption cost associated with the building.

5. The method of claim 4, wherein the energy consumption cost represents an amount of power required to change an indoor temperature of the building for various external temperatures.

6. The method of claim 1, wherein the energy characteristics comprise an indoor temperature of the building and a status of an HVAC system in the building, wherein the HVAC system includes one or more stage heating or cooling units.

7. The method of claim 1, wherein the minimal energy requirements comprise a power consumption amount of an HVAC system in the building and a duty cycle of the HVAC system.

8. The method of claim 1, wherein determining one or more temperature set points is further based upon a comfort preference provided by an occupant of the building.

9. The method of claim 1, further comprising adjusting, by the first computing device, the generated thermal response coefficients for error correction.

10. The method of claim 1, where the selected minimal energy requirements are based on at least a comfort preference provided by an occupant of the building, the method further comprising:

determining, by a second computing device, a second series of temperature set points that change the energy response of the building to use less energy and diverge from the comfort preference; and

transmitting, by a second computing device, the determined temperature set points to the thermostat of the building.

11. The method of claim 10, wherein the second computing device is operated by an energy provider.

12. The method of claim 10, further comprising determining, by the second computing device, an amount of energy saved by changing the energy response of the building to use less energy.

13. The method of claim 10, further comprising translating, by the second computing device, the amount of energy saved into a corresponding energy consumption cost.

14. The method of claim 1, further comprising:

determining, by the first computing device, a price for energy available to be supplied to the building;

determining, by the first computing device, an amount of stored energy associated with the building based on each energy response and the minimal energy requirements; and

transmitting, by the first computing device, an energy consumption action to the thermostat of the building based on the amount of stored energy and the price for energy.

15. The method of claim 1, further comprising controlling the use of temperature-affecting devices in the building based on the series of temperature set points received by the thermostat.

29. The system of claim 1, wherein the first computing device is further configured to

determine a price for energy available to be supplied to the building;

determine an amount of stored energy associated with the building based on each energy response and the minimal energy requirements; and

transmit an energy consumption action to the thermostat of the building based on the amount of stored energy and the price for energy.

16. A system for optimizing and controlling the energy consumption of a building, the system comprising a first computing device configured to

generate thermal response coefficients for the building based on energy characteristics of the building and weather data associated with the location of the building, including:

receiving an indoor air temperature of the building;

inferring physical structure data of the building based upon the location of the building;

determining a transient temperature within one or more walls of the building based upon indoor air temperature and the weather data associated with the location of the building;

adjusting the physical structure data based upon the transient temperature; and

generating the thermal response coefficients for the building based upon the adjusted physical structure data;

predict an energy response of the building for each of a plurality of different points in time during a day, wherein each energy response is based upon the thermal response coefficients and forecasted weather conditions associated with the location of the building for one of the points in time;

select minimal energy requirements of the building based on an energy consumption cost associated with the building;

determine a series of temperature set points for the building for each of the plurality of different points in time based on the corresponding energy response and the minimal energy requirements;

transmit the series of temperature set points to a thermostat of the building, wherein the thermostat adjusts operating parameters of the thermostat using each series of temperature set points when a time value stored in the thermostat matches the point in time associated with each series of temperature set points.

17. The system of claim 16, wherein the physical data comprises at least one of:

thermal mass, wind infiltration, relative area of windows, amount of insulation, material of construction, wind infiltration of the building, and efficiency of an associated HVAC system.

18. The system of claim 16, wherein the first computing device is configured to rank the predicted energy responses based on predetermined criteria.

19. The system of claim 16, wherein predicting an energy response is further based on the energy consumption cost associated with the building.

20. The system of claim 19, wherein the energy consumption cost represents an amount of power required to change an indoor temperature of the building for various external temperatures.

21. The system of claim 16, wherein the energy characteristics comprise an indoor temperature of the building and a status of an HVAC system in the building, wherein the HVAC system includes one or more stage heating or cooling units.

22. The system of claim 16, wherein the minimal energy requirements comprise a power consumption amount of an HVAC system in the building and a duty cycle of the HVAC system.

23. The system of claim 16, wherein determining one or more temperature set points is further based upon a comfort preference provided by an occupant of the building.

24. The system of claim 16, wherein the first computing device is further configured to adjust the generated thermal response coefficients for error correction.

25. The system of claim 16, wherein the selected minimal energy requirements are based on at least a comfort preference provided by an occupant of the building, the system comprises a second computing device configured to:

determine a second series of temperature set points that change the energy response of the building to use less energy and diverge from the comfort preference; and

transmit the determined temperature set points to the thermostat of the building.

26. The system of claim 25, wherein the second computing device is operated by an energy provider.

27. The system of claim 25, wherein the second computing device is further configured to determine an amount of energy saved by changing the energy response of the building to use less energy.

28. The system of claim 25, wherein the second computing device is further configured to translate the amount of energy saved into a corresponding energy consumption cost.

30. A computer program product, tangibly embodied in a non-transitory computer readable storage device, for optimizing and controlling the energy consumption of a building, the computer program product including instructions operable to cause a first computing device to

generate thermal response coefficients for the building based on energy characteristics of the building and weather data associated with the location of the building, including:

receiving an indoor air temperature of the building;

inferring physical structure data of the building based upon the location of the building;

determining a transient temperature within one or more walls of the building based upon indoor air temperature and the weather data associated with the location of the building;

adjusting the physical structure data based upon the transient temperature; and

generating the thermal response coefficients for the building based upon the adjusted physical structure data;

predict an energy response of the building for each of a plurality of different points in time during a day, wherein each energy response is based upon the thermal response coefficients and forecasted weather conditions associated with the location of the building for one of the points in time;

select minimal energy requirements of the building based on an energy consumption cost associated with the building;

determine a series of temperature set points for the building for each of the plurality of different points in time based on the corresponding energy response and the minimal energy requirements;

transmit the series of temperature set points to a thermostat of the building, wherein the thermostat adjusts operating parameters of the thermostat using each series of temperature set points when a time value stored in the thermostat matches the point in time associated with each series of temperature set points.