Optimizing and controlling the energy consumption of a building

• US 9,261,863 B2
• Filed: 12/28/2012
• Issued: 02/16/2016
• 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: Sloop, Christopher Dale, Oberholzer, David, Marshall, Robert S., Kim, Jungho, Siemann, Michael
• Primary Examiner(s): Dunn, Darrin
• Assistant Examiner(s): Wellman, Geoffrey
• Application Number:
  US13/729,501
• Publication Number:
  US 20130190940A1
• Time in Patent Office: 1,145 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 first computing device generates a set of thermal response coefficients for the building based on energy characteristics of the building and weather data associated with the location of the building. The first computing device predicts an energy response of the building based on the set of thermal response coefficients and forecasted weather associated with the location of the building. The first computing device selects minimal energy requirements of the building based on an energy consumption cost associated with the building. The first computing device determines one or more temperature set points for the building based on the energy response and the minimal energy requirements. The first computing device transmits the one or more temperature set points to a thermostat of the building.

Claims

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

generating, by a first computing device, a plurality of thermal response coefficient sets for the building based on energy characteristics of the building and weather data associated with the location of the building, wherein each thermal response coefficient set is generated for a different projection of weather conditions at the location of the building for a given time period, 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 plurality of thermal response coefficient sets for the building based upon the adjusted physical structure data;

predicting, by the first computing device, energy responses of the building, each energy response based on a different set of thermal response coefficients;

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 based on each predicted energy response and the minimal energy requirements; and

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

2. The method of claim 1, wherein the physical structure 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, or 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 energy responses 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 the step of determining a series of temperature set points is further based on a comfort preference provided by an occupant of the building.

9. The method of claim 1, further comprising transmitting, by the first computing device, the predicted energy responses to a remote computing device for display to a user.

10. The method of claim 9, further comprising receiving, by the first computing device from the remote computing device, a temperature preference based on input provided by the user.

11. The method of claim 1, wherein the series of temperature set points transmitted to the thermostat comprise a schedule for control of the thermostat over a given period of time.

12. The method of claim 1, further comprising receiving, by the first computing device, the weather data from a network of remote sensors.

13. The method of claim 1, further comprising receiving, by the first computing device, thermostat data from a device connected to an HVAC system inside the building.

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

15. The method of claim 1, wherein the adjusting step includes filtering anomalies from the generated thermal response coefficient sets.

16. The method of claim 1, wherein the weather data includes current weather conditions at the location of the building, forecast weather conditions for the location of the building, solar load at the location of the building, or any combination thereof.

17. The method of claim 1, further comprising

comparing, by the first computing device, the predicted energy responses of the building to one or more predicted energy responses of one or more other buildings;

and

ranking, by the first computing device, the predicted energy responses of the building based on the comparison.

18. 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 second series of temperature set points to the thermostat of the building.

19. The method of claim 18, wherein the second computing device is operated by an energy provider.

20. The method of claim 18, further comprising determining, by the second computing device, an amount of energy saved by changing the predicted energy responses of the building to use less energy.

21. The method of claim 18, further comprising, by the second computing device, translating the amount of energy saved into a corresponding energy consumption cost.

22. 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 the predicted energy responses 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.

23. The method of claim 22, wherein the amount of stored energy is determined at different points during a given time period.

24. The method of claim 1, wherein the step of generating thermal response coefficient sets for the building is further based on smart meter data.

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

26. The method of claim 25, wherein the temperature-affecting devices include fans, humidifiers, and light shades.

27. The method of claim 1, wherein the weather data associated with the location of the building includes current weather conditions, forecast weather conditions, solar load, wind speed, and outdoor temperature.

28. The method of claim 1, wherein determining a series of temperature set points further comprises

updating, by the first computing device, the energy responses based upon subsequent changes to the weather data;

determining, by the first computing device, an updated series of temperature set points for the building based upon the updated energy responses; and

transmitting, by the first computing device, the updated series of temperature set points to the thermostat.

29. The method of claim 28, wherein the subsequent changes to the weather data include changes to forecasted weather.

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

generate a plurality of thermal response coefficient sets for the building based on energy characteristics of the building and weather data associated with the location of the building, wherein each thermal response coefficient set is generated for a different projection of weather conditions at the location of the building for a given time period, 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; and

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

generating the plurality of thermal response coefficient sets for the building based upon the adjusted physical structure data;

predict energy responses of the building, each energy response based on a different set of thermal response coefficients;

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

determine a series of temperature set points for the building based on each predicted energy response and the minimal energy requirements; and

transmit the series of temperature set points to a thermostat of the building.

31. The system of claim 30, wherein the physical structure 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, or efficiency of an associated HVAC system.

32. The system of claim 30, wherein the first computing device is further configured to rank the predicted energy responses based on predetermined criteria.

33. The system of claim 30, wherein predicting energy responses is further based on the energy consumption cost associated with the building.

34. The system of claim 33, wherein the energy consumption cost represents an amount of power required to change a temperature of the building for various external temperatures.

35. The system of claim 30, 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.

36. The system of claim 30, 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.

37. The system of claim 30, wherein determining the series of temperature set points is further based on weather forecast data, a comfort preference provided by an occupant of the building, or both.

38. The system of claim 30, wherein the first computing device is further configured to transmit the predicted energy responses to a remote computing device for display to a user.

39. The system of claim 38, wherein the first computing device is further configured to receive, from the remote computing device, a temperature preference based on input provided by the user.

40. The system of claim 30, wherein the series of temperature set points transmitted to the thermostat comprise a schedule for control of the thermostat over a period of time.

41. The system of claim 30, wherein the first computing device is further configured to receive the weather data from a network of remote sensors.

42. The system of claim 30, wherein the first computing device is further configured to receive thermostat data from a device connected to an HVAC system inside the building.

43. The system of claim 30, wherein the first computing device is further configured to adjust the generated thermal response coefficient sets for error correction.

44. The system of claim 43, wherein adjusting the generated thermal response coefficient sets includes filtering anomalies.

45. The system of claim 30, wherein the weather data includes current weather conditions at the location of the building, forecast weather conditions for the location of the building, solar load at the location of the building, or any combination thereof.

46. The system of claim 30, wherein the first computing device is further configured to

compare the predicted energy responses of the building to one or more predicted energy responses of one or more other buildings;

and

rank the predicted energy responses of the building based on the comparison.

47. The system of claim 30, wherein the selected minimal energy requirements are based on at least a comfort preference provided by an occupant of the building and a second computing device is 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 second series of temperature set points to the thermostat of the building.

48. The system of claim 47, wherein the second computing device is operated by an energy provider.

49. The system of claim 47, wherein the second computing device is further configured to determine an amount of energy saved by changing the predicted energy responses of the building to use less energy.

50. The system of claim 47, wherein the second computing device is further configured to translate the amount of energy saved into a corresponding energy consumption cost.

51. The system of claim 30, 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 the predicted energy responses 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.

52. The system of claim 51, wherein the amount of stored energy is determined at different points during a given time period.

53. The system of claim 30, wherein generating the thermal response coefficient sets for the building is further based on smart meter data.

54. The system of claim 30, wherein the thermostat is further configured to control use of temperature-affecting devices in the building based on the series of temperature set points received from the first computing device.

55. The system of claim 53, wherein the temperature-affecting devices include fans, humidifiers, and light shades.

56. The system of claim 30, wherein the weather data associated with the location of the building includes current weather conditions, forecast weather conditions, solar load, wind speed, and outdoor temperature.

57. The system of claim 30, wherein when determining a series of temperature set points, the first computing device is further configured to

update the energy responses based upon subsequent changes to the weather data;

determine an updated series of temperature set points for the building based upon the updated energy responses; and

transmit the updated series of temperature set points to the thermostat.

58. The system of claim 57, wherein the subsequent changes to the weather data include changes to forecasted weather.

59. A computer program product, tangibly embodied in a non-transitory computer readable storage medium, 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 a plurality of thermal response coefficient sets for the building based on energy characteristics of the building and weather data associated with the location of the building, wherein each thermal response coefficient set is generated for a different projection of weather conditions at the location of the building for a given time period, 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; and

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

generating the plurality of thermal response coefficient sets for the building based upon the adjusted physical structure data;

predict energy responses of the building, each energy response based on a different set of thermal response coefficients;

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

determine a series of temperature set points for the building based on each predicted energy response and the minimal energy requirements; and

transmit the series of temperature set points to a thermostat of the building.