Radiant heating controls and methods for an environmental control system

• US 8,600,561 B1
• Filed: 09/30/2012
• Issued: 12/03/2013
• Est. Priority Date: 09/30/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Nest Labs Incorporated (Alphabet Inc.)
• Inventors: Matsuoka, Yoky, Malhotra, Mark, Modi, Yash
• Primary Examiner(s): Bahta, Kidest
• Application Number:
  US13/632,152
• Time in Patent Office: 429 Days
• Field of Search: 700/276, 700/277, 700/299, 236/1.C, 236/93, 236/46.R, 219/502
• US Class Current: 700/276
• 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 thermostat comprising:

a housing;

a memory; and

a processing system disposed within the housing, the processing system being in operative communication with one or more temperature sensors to determine an ambient temperature in an enclosure, the processing system being in operative communication with the memory, and the processing system being in operative communication with a radiant heating system to heat the enclosure via radiant heating such that the ambient temperature is near a setpoint temperature, wherein control of the radiant heating system is performed in part by the processing system;

determining a parameterized model from which a predicted value for the ambient temperature of the enclosure responsive to a candidate radiant heating control strategy may be determined, the parameterized model being based on historical ambient temperatures for the enclosure acquired by the thermostat during associated historical periods in which radiant heat control was actuated by the thermostat and stored in said memory;

selecting a set of candidate control strategies for use in controlling the radiant heating system, wherein each said candidate control strategy is a binary-valued control trajectory having a candidate overall on-time percentage over a predefined candidate control duration, wherein each said candidate control strategy is constrained to have a minimum number of on-time cycles that achieves the candidate overall on-time percentage;

executing a predictive algorithm to determine an optimal control strategy from the set of candidate control strategies by;

applying each candidate control strategy to the parameterized model to predict a corresponding ambient temperature trajectory; and

processing each said corresponding ambient temperature trajectories in view of one or more predetermined assessment functions to select an optimal one of said candidate control strategies according to one or more predetermined assessment criteria; and

controlling the radiant heating system according to the selected optimal control strategy.

2. The thermostat of claim 1, wherein each candidate control strategy exhibits a single on-time to off-time cycle transition over the predefined candidate control duration, or wherein the radiant heating system does not perform an on-time to off-time cycle transition more than twice during the predefined candidate control duration.

3. The thermostat of claim 1, wherein the one or more predetermined assessment functions comprises a cost function in which a cost is increased as an ambient temperature trajectory of a respective candidate control strategy deviates from the setpoint temperature.

4. The thermostat of claim 1, further comprising determining a Lag value that represents at least in part an amount of system inertia for the enclosure.

5. The thermostat of claim 1, wherein the on-time cycles and off-time cycles comprise intervals of not less than 10 minutes.

6. The thermostat of claim 1, wherein the parameterized model comprises predetermined response trajectories, and wherein the parameterized model is determined by finding weighting coefficients of the predetermined response trajectories.

7. The thermostat of claim 1, wherein the parameterized model is further based on a combination of historical solar radiation and a radiant heating response data acquired during associated historical periods, and wherein applying each candidate control strategy to the parameterized model comprises using a solar radiation function and a radiant heating response function to predict the corresponding ambient temperature trajectory.

8. The thermostat of claim 1, wherein the parameterized model is further based on historical outside temperature data acquired during associated historical periods, and wherein applying each candidate control strategy to the parameterized model comprises using forecasted temperature data to predict the corresponding ambient temperature trajectory.

9. The thermostat of claim 1, wherein the parameterized model is further based on historical data acquired during associated historical periods for one or more of the following data types:

seasonal climate change data, humidity data, rainfall data, snowpack data, or elevation data; and

wherein applying each candidate control strategy to the parameterized model comprises using forecasted data or otherwise selected data for the one or more data types to predict the corresponding ambient temperature trajectory.

10. The thermostat of claim 1, further comprising limiting a cycle transition of the radiant heating system while the ambient temperature is outside of a defined maintenance band of the setpoint temperature.

11. The thermostat of claim 1, further comprising increasing an offset value of a maintenance band that defines an upper threshold temperature and a lower threshold temperature relative to the setpoint temperature based on an increased confidence that the parameterized model characterizes the historical ambient temperatures.

12. A method of controlling a thermostat using model predictive control comprising:

providing a thermostat comprising;

a housing;

a memory; and

a processing system disposed within the housing, the processing system being in operative communication with one or more temperature sensors to determine an ambient temperature in an enclosure, the processing system being in operative communication with the memory, and the processing system being in operative communication with a radiant heating system to heat the enclosure via radiant heating such that the ambient temperature is near a setpoint temperature;

determining a parameterized model from which a predicted value for the ambient temperature of the enclosure responsive to a candidate radiant heating control strategy may be determined, the parameterized model being based on historical ambient temperatures for the enclosure acquired by the thermostat during associated historical periods in which radiant heat control was actuated by the thermostat and stored in said memory;

selecting a set of candidate control strategies for use in controlling the radiant heating system, wherein each said candidate control strategy is a binary-valued control trajectory having a candidate overall on-time percentage over a predefined candidate control duration, wherein each said candidate control strategy is constrained to have a minimum number of on-time cycles that achieves the candidate overall on-time percentage;

executing a predictive algorithm to determine an optimal control strategy from the set of candidate control strategies by;

applying each candidate control strategy to the parameterized model to predict a corresponding ambient temperature trajectory; and

processing each said corresponding ambient temperature trajectories in view of one or more predetermined assessment functions to select an optimal one of said candidate control strategies according to one or more predetermined assessment criteria; and

controlling the radiant heating system according to the selected optimal control strategy.

13. The method of claim 12, further comprising determining whether the model predictive control provides enhanced control of the radiant heating system relative to an additional control method prior to using the model predictive control.

14. The method of claim 12, wherein the one or more predetermined assessment functions comprises a cost function in which a cost is increased as an ambient temperature trajectory of a respective candidate control strategy deviates from the setpoint temperature.

15. The method of claim 12, further comprising determining a Lag value that represents at least in part an amount of system inertia for the enclosure.

16. The method of claim 12, wherein the on-time cycles and off-time cycles comprise intervals of not less than 10 minutes.

17. The method of claim 12, wherein the parameterized model comprises predetermined response trajectories, and wherein the method further comprises determining weighting coefficients of the predetermined response trajectories.

18. The method of claim 12, wherein the parameterized model is further based on a combination of historical solar radiation and a radiant heating response data acquired during associated historical periods, and wherein applying each candidate control strategy to the parameterized model comprises using a solar radiation function and a radiant heating response function to predict the corresponding ambient temperature trajectory.

19. The method of claim 12, wherein the parameterized model is further based on historical outside temperature data acquired during associated historical periods, and wherein applying each candidate control strategy to the parameterized model comprises using forecasted temperature data to predict the corresponding ambient temperature trajectory.

20. The method of claim 12, further comprising limiting a cycle transition of the radiant heating system while the ambient temperature is outside of a defined maintenance band of the setpoint temperature.