OPTIMIZING AND CONTROLLING THE ENERGY CONSUMPTION OF A BUILDING

• US 20130190940A1
• Filed: 12/28/2012
• Published: 07/25/2013
• 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, Marshall, Robert S., Kim, Jungho, Oberholzer, David, Siemann, Michael
• Granted Patent: US 9,261,863 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• 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 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;

predicting, by the first computing device, an energy response of the building based on the set of thermal response coefficients and forecasted weather associated with the location of the building;

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, one or more temperature set points for the building based on the energy response and the minimal energy requirements; and

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

2. The method of claim 1, wherein generating the set of thermal response coefficients is further based on physical data of the building.

3. The method of claim 2, 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.

4. The method of claim 1, further comprising ranking the predicted energy response based on predetermined criteria.

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

6. The method of claim 5, the energy consumption cost representing an amount of power required to change a temperature of the building for various external temperatures.

7. 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.

8. 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.

9. The method of claim 1, wherein determining one or more temperature set points is further based on weather forecast data, a comfort preference provided by an occupant of the building, or both.

10. The method of claim 1, further comprising:

transmitting, by the first computing device, the predicted energy response to a remote computing device for display to a user.

11. The method of claim 10, further comprising:

receiving, from the remote computing device, a temperature preference based on input provided by the user.

12. The method of claim 1, wherein the one or more temperature set points transmitted to the thermostat comprise a schedule for control of the thermostat over a period of time.

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

14. The method of claim 13, wherein the weather data is received in real time.

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

16. The method of claim 15, wherein the thermostat data is received at predetermined time intervals.

17. The method of claim 15, wherein the thermostat data is received in real time.

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

19. The method of claim 1, the adjusting including filtering anomalies from the generated set of thermal response coefficients.

20. 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.

21. The method of claim 1, further comprising:

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

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

22. The method of claim 1, where the selected optimal 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, one or more 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.

23. The method of claim 22, wherein the second computing device is operated by an energy provider.

24. The method of claim 22, wherein the first computing device and the second computing device are the same.

25. The method of claim 22, further comprising determining an amount of energy saved by changing the energy response of the building to use less energy.

26. The method of claim 22, further comprising translating the amount of energy saved into a corresponding energy consumption cost.

27. The method of claim 1, further comprising:

determining, by a 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 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.

28. The method of claim 27, wherein the amount of stored energy is determined at different points during a given time period.

29. The method of claim 1, wherein generating a set of thermal response coefficients for the building is further based on smart meter data.

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

31. The method of claim 30, wherein the temperature-affecting devices include fans, humidifiers, and light shades.

32. A computerized system for optimizing and controlling the energy consumption of a building, the system comprising:

a first computing device configured to;

generate 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;

predict an energy response of the building based on the set of thermal response coefficients and forecasted weather associated with the location of the building;

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

determine one or more temperature set points for the building based on the energy response and the minimal energy requirements; and

transmit the one or more temperature set points to a thermostat of the building.

33. 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 data processing apparatus to:

generate 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;

predict an energy response of the building based on the set of thermal response coefficients and forecasted weather associated with the location of the building;

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

determine one or more temperature set points for the building based on the energy response and the minimal energy requirements; and

transmit the one or more temperature set points to a thermostat of the building.

34. A system for optimizing and controlling the energy consumption of a building, the system comprising:

means for generating 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;

means for predicting an energy response of the building based on the set of thermal response coefficients and forecasted weather associated with the location of the building;

means for selecting minimal energy requirements of the building based on an energy consumption cost associated with the building;

means for determining one or more temperature set points for the building based on the energy response and the minimal energy requirements; and

means for transmitting the one or more temperature set points to a thermostat of the building.

35. A method for computing the stored energy in a building, the method comprising:

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

generating, by the first computing device, 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;

predicting, by the first computing device, an energy response of the building based on the set of thermal response coefficients and forecasted weather associated with the location of the building;

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, one or more temperature set points for the building based on the energy response and the minimal energy requirements; and

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