Heating controls and methods for an environmental control system

• US 10,012,407 B2
• Filed: 01/13/2015
• Issued: 07/03/2018
• Est. Priority Date: 09/30/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Modi, Yash, Matsuoka, Yoky, Malhotra, Mark
• Primary Examiner(s): Bahta, Kidest
• Application Number:
  US14/596,059
• Publication Number:
  US 20150134122A1
• Time in Patent Office: 1,267 Days
• Field of Search: 700276
• 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 11/523 for displaying temperature data
• CPC Class Codes: F24F 11/58 using Internet communication
• 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 11/65 for selecting an operating mode
• CPC Class Codes: F24F 11/88 Electrical aspects, e.g. circuits
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: G05B 13/04 involving the use of models or simulators
• CPC Class Codes: G05D 23/19 characterised by the use of electric means G05D23/1393 takes precedence
• CPC Class Codes: G05D 23/1919 characterised by the type of controller
• CPC Class Codes: G06N 5/048 Fuzzy inferencing

Abstract

Embodiments of the invention describe thermostats that use model predictive controls and related methods. A method of controlling a thermostat using a model predictive control may involve determining a parameterized model. The parameterized model may be used to predicted ambient temperature values for an enclosure. A set of radiant heating system control strategies may be selected for evaluation to determine an optimal control strategy from the set of control strategies. To determine the optimal control strategy, a predictive algorithm may be executed, in which each control strategy is applied to the parameterized model to predict an ambient temperature trajectory and each ambient temperature trajectory is processed in view of a predetermined assessment function. Processing the ambient temperature trajectory in this manner may involve minimizing a cost value associated with the ambient temperature trajectory. The radiant heating system may subsequently be controlled according to the selected optimal control strategy.

Claims

1. A heating, ventilating, and air conditioning (HVAC) control system comprising:

a processing system in operative communication with a heating system, the processing system being configured and programmed to control the heating system to;

acquire historical temperature information regarding heating of an enclosure during at least one historical period in which the enclosure was heated by the heating system under the control of said HVAC control system;

determine a lag value that represents at least in part an amount of system inertia for the enclosure;

determine a plurality of candidate heating control strategies based on said historical temperature information;

determine an optimal heating control strategy from said plurality of candidate heating control strategies by computing a plurality of predicted temperature responses corresponding respectively to the plurality of candidate heating control strategies; and

control the heating system according to the determined optimal heating control strategy.

2. The HVAC control system of claim 1, wherein the HVAC control system includes a housing and memory, the memory being disposed within the housing and including instructions that program the processing system to perform said steps.

3. The HVAC control system of claim 1, wherein information for said processing system is stored remotely from the HVAC control system.

4. The HVAC control system of claim 1, wherein the processing system includes a first high-powered processor that is configured and programmed to determine the optimal heating control strategy, and wherein the processing system includes a second low-powered processor that is in operative communication with one or more temperature sensors to determine an ambient temperature.

5. The HVAC control system of claim 4, wherein the HVAC control system includes a rechargeable battery and the first processor is configured to transition between a wake state and a sleep state, wherein each time the first processor transitions from the sleep state to the wake state, the optimal heat control strategy is re-determined based at least in part on ambient temperature readings determined by the second processor during the sleep state of the first processor.

6. The HVAC control system of claim 1, wherein said processing system is further configured and programmed to control the heating system to determine a parameterized model from which the plurality of candidate heating control strategies is determined, the parameterized model being based at least in part on the historical temperature information.

7. The HVAC control system of claim 1, wherein a first optimal heat strategy covering a first time period is determined and executed to control the heating system during the first time period, and wherein a second optimal heat control strategy is determined during the first time period and executed prior to an end of the first time period to control the heating system.

8. The HVAC control system of claim 1, wherein said processing system is further configured and programmed to control the heating system to process said predicted temperature responses according to one or more predetermined assessment criteria.

19. The HVAC control system of claim 1, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on user entry in a user interface of the HVAC control system of a heating control preference.

20. The HVAC control system of claim 1, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on an automated wire insertion detection algorithm.

21. The HVAC control system of claim 1, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on trial runs of the HVAC control system at or near the time of installation of the HVAC control system.

22. The HVAC control system of claim 1, wherein the heating system is a heating system.

9. A method of controlling a heating, ventilating, and air conditioning (HVAC) control system comprising:

providing a heating, ventilating, and air conditioning (HVAC) control system that includes a processing system in operative communication with a heating system;

acquiring historical temperature information regarding heating of an enclosure during at least one historical period in which the enclosure was heated by the heating system under the control of said HVAC control system;

determining a lag value that represents at least in part an amount of system inertia for the enclosure;

determining a plurality of candidate heating control strategies based on said historical temperature information;

determining an optimal heating control strategy from said plurality of candidate heating control strategies by computing a plurality of predicted temperature responses corresponding respectively to the plurality of candidate heating control strategies; and

controlling the heating system according to the determined optimal heating control strategy.

10. The method of claim 9, wherein information for said processing system is stored remotely from the HVAC control system.

11. The method of claim 9, wherein the processing system includes a first high-powered processor that is configured and programmed to determine the optimal heating control strategy, and wherein the processing system includes a second low-powered processor that is in operative communication with one or more temperature sensors to determine an ambient temperature.

12. The method of claim 11, wherein the HVAC control system includes a rechargeable battery and the first processor is configured to transition between a wake state and a sleep state, wherein each time the first processor transitions from the sleep state to the wake state, the optimal heat control strategy is re-determined based at least in part on ambient temperature readings determined by the second processor during the sleep state of the first processor.

13. The method of claim 9, wherein the method further includes determining a parameterized model from which the plurality of candidate heating control strategies is determined, the parameterized model being based at least in part on the historical temperature information.

23. The method of claim 9, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on user entry in a user interface of the HVAC control system of a heating control preference.

24. The method of claim 9, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on an automated wire insertion detection algorithm.

25. The method of claim 9, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on trial runs of the HVAC control system at or near the time of installation of the HVAC control system.

26. The method of claim 9, wherein the heating system is a radiant heating system.

14. A computer-program product, tangibly embodied in a non-transitory machine readable storage medium, including instructions configured to cause a data processing apparatus of an HVAC control system to:

acquire historical temperature information regarding heating of an enclosure during at least one historical period in which the enclosure was heated by a heating system under the control of said HVAC control system;

determine a lag value that represents at least in part an amount of system inertia for the enclosure;

determine a plurality of candidate heating control strategies based on said historical temperature information;

determine an optimal heating control strategy from said plurality of candidate heating control strategies by computing a plurality of predicted temperature responses corresponding respectively to the plurality of candidate heating control strategies; and

control the heating system according to the determined optimal heating control strategy.

15. The computer-program product of claim 14, wherein the HVAC control system includes a first high-powered processor that is configured and programmed to determine the optimal heating control strategy, and wherein the HVAC control system includes a second low-powered processor that is in operative communication with one or more temperature sensors to determine an ambient temperature of the enclosure.

16. The computer-program product of claim 15, wherein the HVAC control system includes a rechargeable battery and the first processor is configured to transition between a wake state and a sleep state, wherein each time the first processor transitions from the sleep state to the wake state, the optimal heat control strategy is re-determined based at least in part on ambient temperature readings determined by the second processor during the sleep state of the first processor.

17. The computer-program product of claim 14, wherein the instructions are further configured to cause the data processing apparatus to determine a parameterized model from which the plurality of candidate heating control strategies is determined, the parameterized model being based at least in part on the historical temperature information.

18. The computer-program product of claim 14, wherein the instructions are further configured to cause the data processing apparatus to:

determine a first optimal heat strategy covering a first time period;

execute the first optimal heat strategy to control the heating system during the first time period;

determine a second optimal heat control strategy during the first time period; and

execute the second optimal heat strategy prior to an end of the first time period to control the heating system.

27. The computer-program product of claim 14, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on user entry in a user interface of the HVAC control system of a heating control preference.

28. The computer-program product of claim 14, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on an automated wire insertion detection algorithm.

29. The computer-program product of claim 14, wherein said determining the optimal heating control strategy is performed pursuant to a determination that such control is desired based on trial runs of the HVAC control system at or near the time of installation of the HVAC control system.

30. The computer-program product of claim 14, wherein the heating system is a radiant heating system.