Optimizing and controlling the energy consumption of a building

• US 9,612,591 B2
• Filed: 01/02/2015
• Issued: 04/04/2017
• 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): SHECHTMAN, SEAN P
• Application Number:
  US14/588,699
• Publication Number:
  US 20150192911A1
• Time in Patent Office: 823 Days
• Field of Search: None
• CPC Class Codes: F24F 2110/12 of the outside air
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• 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: Y04S 20/244 the home appliances being or involving heating ventilating and air conditioning [HVAC] units

Abstract

Described herein are methods and systems, including computer program products, for optimizing and controlling a building'"'"'s energy consumption and comfort. A computing device receives measurements from a plurality of sensors, at least some of which are located inside the building, where the measurements include temperature readings and comfort characteristics. The computing device generates a set of thermal response coefficients based on energy characteristics of the building, the measurements from the sensors, and weather data associated with the building'"'"'s location. The computing device predicts an energy response of the building based on the set of thermal response coefficients and forecasted weather. The computing device selects minimal energy requirements of the building based on an energy consumption cost associated with the building and determines energy control points based on the energy response and the minimal energy requirements. The computing device transmits the energy control points to comfort devices in the building.

Claims

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

receiving, by a first computing device, one or more measurements from a plurality of sensors, at least some of which are located inside the building, wherein the measurements include temperature readings and comfort characteristics;

generating, by the first computing device, a set of thermal response coefficients for the building based on energy characteristics of the building, the measurements from the sensors 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;

determining, by the first computing device, a series of energy control points for each of a plurality of comfort devices in the building based on the energy response, wherein the series of energy control points are operable to adjust one or more operational settings of the comfort devices in the building;

optimizing, by the first computing device, the series of energy control points based upon an estimated energy cost for each of a plurality of predetermined time periods so that the corresponding comfort devices operate to consume less energy during time periods that have a high estimated energy cost and the corresponding comfort devices operate to consume more energy during time periods that have a low estimated energy cost;

transmitting, by the first computing device, the energy control points to one or more comfort devices in the building;

detecting, by the first computing device for each of the plurality of predetermined time periods, temperature readings from one or more sensors each inside different rooms of the building, temperature readings from each of one or more sensors outside the building, and temperature readings provided by a thermostat inside the building;

determining, by the first computing device, whether the temperature readings from any of the one or more sensors inside different rooms of the building diverge over time from the temperature readings from the one or more sensors outside the building and from the temperature readings from the thermostat; and

if at least one of the one or more sensors inside different rooms of the building diverge over time from the temperature readings from the one or more sensors outside the building and from the temperature readings from the thermostat;

adjusting, by the first computing device, the series of energy control points for one or more comfort devices associated with the room of the diverging sensor to account for the divergence in temperature.

2. The method of claim 1, wherein the energy control points include thermostat set points, control settings for the comfort devices, and control settings for window shades.

3. The method of claim 1, wherein the comfort characteristics include occupancy status of a building area, humidity, radiative heat from walls, operational status for the comfort devices, a location of a building occupant, a distance of the building occupant from the building, and a travel time for the occupant to arrive at the building.

4. The method of claim 3, further comprising adjusting, by the first computing device, the series of energy control points based upon the travel time and/or the distance.

5. The method of claim 1, wherein the energy characteristics include one or more temperature readings from the sensors, a temperature reading from a thermostat of the building, a status of an HVAC system in the building, a status of one or more energy sources supplying the building, and status of doors and/or windows of the building.

6. The method of claim 5, wherein the HVAC system includes one or more stage heating or cooling units.

7. The method of claim 5, wherein the energy sources supplying the building include electric, gas, solar, wind, heat pump, and energy storage devices.

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

9. The method of claim 8, 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.

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

11. The method of claim 10, wherein the energy consumption cost represents an amount of power required to change a comfort level of the building for various external temperatures.

12. The method of claim 1, wherein determining the series of energy control points is further based on weather forecast data, a comfort preference provided by an occupant of the building, or both.

13. The method of claim 1, wherein the series of energy control points transmitted to the thermostat comprise a schedule for control of the thermostat'"'"'s temperature setting over a period of time.

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

15. 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.

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

17. The method of claim 16, wherein the error correction includes filtering anomalies from the generated set of thermal response coefficients.

18. 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.

19. 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 of the predicted energy response.

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

21. The method of claim 1, wherein the plurality of sensors include combination door status and temperature sensors, combination window status and temperature sensors, combination appliance status and temperature sensors, combination motion detection and temperature sensors, infrared thermal sensors, standalone temperature sensors, and humidity sensors.

22. The method of claim 21, further comprising

receiving, by the first computing device, a signal from a combination door status and temperature sensor;

determining, by the first computing device, whether a door associated with the combination door status and temperature sensor is open or closed based upon the signal; and

identifying, by the first computing device, an energy loss issue for the door if the door is closed and a temperature reading from the combination door status and temperature sensor diverges from a temperature measurement of a thermostat in the building.

23. The method of claim 22, further comprising transmitting, by the first computing device, an alert to a remote computing device associated with an occupant of the building if an energy loss issue is identified.

24. The method of claim 23, wherein the alert includes an energy efficiency scorecard for the building and identifies the energy loss issue.

25. The method of claim 21, further comprising

receiving, by the first computing device, a motion detection signal and a temperature signal from one or more sensors;

determining, by the first computing device, an occupancy status of an area monitored by the one or more sensors based upon the motion detection signal; and

adjusting, by the first computing device, the series of energy control points based upon the occupancy status and the temperature signal.

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

receive one or more measurements from a plurality of sensors, at least some of which are located inside the building, wherein the measurements include temperature readings and comfort characteristics;

generate a set of thermal response coefficients for the building based on energy characteristics of the building, the measurements from the sensors, 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;

determine a series of energy control points for each of a plurality of comfort devices in the building based on the energy response, wherein the series of energy control points are operable to adjust one or more operational settings of comfort devices in the building;

optimize the series of energy control points based upon an estimated energy cost for each of a plurality of predetermined time periods so that the corresponding comfort devices operate to consume less energy during time periods that have a high estimated energy cost and the corresponding comfort devices operate to consume more energy during time periods that have a low estimated energy cost;

transmit the energy control points to one or more comfort devices in the building;

detect, for each of the plurality of predetermined time periods, temperature readings from one or more sensors each inside different rooms of the building, temperature readings from each of one or more sensors outside the building, and temperature readings provided by a thermostat inside the building;

determine whether the temperature readings from any of the one or more sensors inside different rooms of the building diverge over time from the temperature readings from the one or more sensors outside the building and from the temperature readings from the thermostat; and

if at least one of the one or more sensors inside different rooms of the building diverge over time from the temperature readings from the one or more sensors outside the building and from the temperature readings from the thermostat;

adjust the series of energy control points for one or more comfort devices associated with the room of the diverging sensor to account for the divergence in temperature.

27. The system of claim 26, wherein the energy control points include thermostat set points, control settings for the comfort devices, and control settings for window shades.

28. The system of claim 26, wherein the comfort characteristics include occupancy status of a building area, humidity, radiative heat from walls, operational status for the comfort devices, a location of a building occupant, a distance of the building occupant from the building, and a travel time for the occupant to arrive at the building.

29. The system of claim 28, wherein the first computing device is further configured to adjust the series of energy control points based upon the travel time and/or the distance.

30. The system of claim 26, wherein the energy characteristics include one or more temperature readings from the sensors, a temperature reading from a thermostat of the building, a status of an HVAC system in the building, a status of one or more energy sources supplying the building, and status of doors and/or windows of the building.

31. The system of claim 30, wherein the HVAC system includes one or more stage heating or cooling units.

32. The system of claim 30, wherein the energy sources supplying the building include electric, gas, solar, wind, heat pump, and energy control devices.

33. The system of claim 26, wherein generating the set of thermal response coefficients is further based on physical data of the building.

34. The system of claim 33, 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.

35. The system of claim 26, wherein predicting an energy response is further based on an energy consumption cost associated with the building.

36. The system of claim 35, wherein the energy consumption cost represents an amount of power required to change a comfort level of the building for various external temperatures.

37. The system of claim 35, wherein determining the series of energy control 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 35, wherein the series of energy control points transmitted to the thermostat comprise a schedule for control of the thermostat'"'"'s temperature setting over a period of time.

39. The system of claim 35, wherein the first computing device is further configured to the weather data from a network of remote sensors.

40. The system of claim 35, wherein the first computing device receives thermostat data from a device connected to an HVAC system inside the building.

41. The system of claim 35, wherein the first computing device adjusts the generated set of thermal response coefficients using error correction.

42. The system of claim 41, wherein the error correction includes filtering anomalies from the generated set of thermal response coefficients.

43. The system of claim 35, 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.

44. The system of claim 35, wherein the first computing device is further configured to

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

and

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

45. The system of claim 35, wherein generating a set of thermal response coefficients for the building is further based on smart meter data.

46. The system of claim 35, wherein the plurality of sensors include combination door status and temperature sensors, combination window status and temperature sensors, combination appliance status and temperature sensors, combination motion detection and temperature sensors, infrared thermal sensors, standalone temperature sensors, and humidity sensors.

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

receive a signal from a combination door status and temperature sensor;

determine whether a door associated with the combination door status and temperature sensor is open or closed based upon the signal; and

identify an energy loss issue for the door if the door is closed and a temperature reading from the combination door status and temperature sensor diverges from a temperature measurement of a thermostat in the building.

48. The system of claim 47, wherein the first computing device is further configured to transmit an alert to a remote computing device associated with an occupant of the building if an energy loss issue is identified.

49. The system of claim 48, wherein the alert includes an energy efficiency scorecard for the building and identifies the energy loss issue.

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

receive a motion detection signal and a temperature signal from one or more sensors;

determine an occupancy status of an area monitored by the one or more sensors based upon the motion detection signal; and

adjust the series of energy control points based upon the occupancy status and the temperature signal.

51. 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:

receive one or more measurements from a plurality of sensors, at least some of which are located inside the building, wherein the measurements include temperature readings and comfort characteristics;

generate a set of thermal response coefficients for the building based on energy characteristics of the building, the measurements from the sensors, 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;

determine a series of energy control points for each of a plurality of comfort devices in the building based on the energy response, wherein the series of energy control points are operable to adjust one or more operational settings of comfort devices in the building;

optimize the series of energy control points based upon an estimated energy cost for each of a plurality of predetermined time periods so that the corresponding comfort devices operate to consume less energy during time periods that have a high estimated energy cost and the corresponding comfort devices operate to consume more energy during time periods that have a low estimated energy cost;

transmit the energy control points to one or more comfort devices in the building;

detect, for each of the plurality of predetermined time periods, temperature readings from one or more sensors each inside different rooms of the building, temperature readings from each of one or more sensors outside the building, and temperature readings provided by a thermostat inside the building;

determine whether the temperature readings from any of the one or more sensors inside different rooms of the building diverge over time from the temperature readings from the one or more sensors outside the building and from the temperature readings from the thermostat; and

if at least one of the one or more sensors inside different rooms of the building diverge over time from the temperature readings from the one or more sensors outside the building and from the temperature readings from the thermostat;

adjust the series of energy control points for one or more comfort devices associated with the room of the diverging sensor to account for the divergence in temperature.