OPERATION OF A THERMAL COMFORT SYSTEM

• US 20140027103A1
• Filed: 07/26/2012
• Published: 01/30/2014
• Est. Priority Date: 07/26/2012
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Marik, Karel, Strelec, Martin
• Granted Patent: US 9,074,785 B2
• Time in Patent Office: Days
• US Class Current: 165/239
• 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 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/52 Indication arrangements, e.g. displays
• 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 11/67 Switching between heating and cooling modes
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: G05D 23/1951 with control of the working time of a temperature controlling device

Abstract

Systems, methods, and devices for operation of a thermal comfort system are described herein. For example, one or more embodiments include receiving data, including a target temperature for a zone, an actual temperature of the zone, and an ambient temperature of air being supplied to a thermal comfort system, and determining, through a regression model, from the received data, a calculated time when the zone will reach the target temperature upon operation of the thermal comfort system.

Claims

1. A method, comprising:

receiving data, including a target temperature for a zone, an actual temperature of the zone, and an ambient temperature of air being supplied to a thermal comfort system; and

determining, through a regression model, from the received data, a calculated time when the zone will reach the target temperature upon operation of the thermal comfort system.

2. The method of claim 1, wherein the method includes calculating the time when the zone will reach the target temperature through the regression model:

t_pred−

(T_aT−

T_a)·

p₁+(T_aT−

T_a)²·

p₂+(T_aT−

T_o)·

p₃+p₄, wherein;

t_pred is the calculated time when the zone will reach the target temperature;

T_aT is the target temperature for the zone;

T_a is the actual temperature of the zone;

T_o is the ambient temperature of air being supplied to the thermal comfort system; and

p₂, p₃, and p₄ are coefficients of the regression model.

3. The method of claim 1, wherein the method includes receiving the target temperature for the zone through a user interface of a temperature control system.

4. The method of claim 1, wherein the method includes receiving the ambient temperature at the temperature control system from a temperature sensor.

5. The method of claim 2, wherein the method includes:

initializing the regression model by setting p₂, p₃, and p₄ to zero and entering a cooling rate of the thermal comfort system for p_i; and

solving for t_pred to obtain an initial calculated cooling time when the zone will reach the target temperature.

6. The method of claim 2, wherein the method includes:

initializing the regression model by setting p₂, p₃, and p₄ to zero and entering a heating rate of the thermal comfort system for p_i; and

solving for t_pred to obtain an initial calculated heating time when the zone will reach the target temperature.

7. The method of claim 1, wherein the method includes:

operating the thermal comfort system for the initial calculated time when the zone will reach the target temperature; and

receiving updated values for an actual time for the zone to reach the target temperature, the actual temperature of the zone, and the ambient temperature of air being supplied to the thermal comfort system upon operation of the thermal comfort system.

8. The method of claim 7, wherein the method includes solving for p₁, p₂, p₃, and p₄ using the target temperature for the zone and the updated values for the actual time for the zone to reach the target temperature, the actual temperature of the zone, and the ambient temperature of air being supplied to the thermal comfort system.

9. The method of claim 8, wherein the method includes updating at least one of the coefficients of the regression model with each run cycle of the thermal comfort system.

10. The method of claim 8, wherein updating at least one of the coefficients of the regression model includes:

determining a covariance matrix for the regression model; and

selecting eigenvalues of the covariance matrix to adjust when at least one of the coefficients of the regression model is updated.

11. The method of claim 10, wherein coefficients of the regression model associated with eigenvalues that are smaller, relative to eigenvalues associated with remaining coefficients of the regression model, are updated later.

13. The medium of claim 1, wherein the instructions executable by the machine to determine the calculated time when the zone will reach the target temperature include instructions to determine the calculated time through the regression model:

14. The medium of claim 13, wherein the instructions include instructions executable by the machine to determine an initial calculated time when the zone will reach the target temperature by setting p_nTa+i, p_nTA+nTo+i, and p_nTa+nTo+nTm to a value of zero and entering at least one of a heating rate and cooling rate of the thermal comfort system for p_i.

15. The medium of claim 13, wherein the instructions include instructions executable by the machine to:

receive updated values for an actual time for the zone to reach the target temperature, the actual temperature of the zone, the ambient temperature of air being supplied to the thermal comfort system, and the temperature of the media supplied to the thermal comfort system; and

solve for p_i, p_nTa+i, p_nTA+nTo+i, and p_nTa+nTo+nTm, based on the received updated values, using a recursive least squares method.

16. The medium of claim 15, wherein the instructions include instructions executable by the machine to:

determine a covariance matrix for the regression model; and

update at least one of the coefficients of the regression model by performing a comparison of a size of eigenvalues in the covariance matrix.

17. The medium of claim 16, wherein the coefficient of the regression model associated with the smallest eigenvalue is updated after the other coefficients of the regression model associated with larger eigenvalues.

12. A machine-readable non-transitory medium storing instructions, executable by a machine to cause the machine to:

receive a target temperature for a zone, an actual temperature of the zone, and an ambient temperature of air being supplied to a thermal comfort system; and

determine, from the received target temperature for the zone, the actual temperature of the zone, the ambient temperature of air being supplied to the thermal comfort system, and a temperature of a media supplied to the thermal comfort system, a calculated time when the zone will reach the target temperature upon operation of the thermal comfort system.

18. A system, the system comprising a processing resource in communication with a non-transitory computer-readable medium, wherein the computer-readable medium contains a set of instructions and wherein the processing resource is designed to execute the set of instructions to:

receive a target temperature for a zone, an actual temperature of the zone, and an ambient temperature of air being supplied to a thermal comfort system;

determine, from the target temperature for the zone, the actual temperature of the zone, and the ambient temperature of air being supplied to the thermal comfort system, a calculated time when the zone will reach the target temperature upon operation of the thermal comfort system; and

operate the thermal comfort system for the calculated time.

19. The system of claim 18, wherein a regression model is used to determine the calculated time when the zone will reach the target temperature upon operation of the thermal comfort system.

20. The system of claim 19, wherein the regression model is updated with each run cycle of the thermal comfort system.