Building management system with triggered feedback set-point signal for persistent excitation

1. An environmental control system for a building, the system comprising:

heating, ventilation, or air conditioning (HVAC) equipment that operates to affect a temperature of a zone of the building;

a temperature sensor to measure the temperature of the zone; and

a controller comprising a processing circuit configured to:

operate the HVAC equipment based on a temperature setpoint;

gather training data indicating the temperature of the zone and the temperature setpoint;

monitor a temperature tracking error of the zone and a heat transfer value of the HVAC equipment, the temperature tracking error based on the training data;

determine if the HVAC equipment is in a saturation region based on the temperature tracking error and the heat transfer value;

in response to a determination that the HVAC equipment is in the saturation region, calculate an adjusted temperature setpoint; and

operate the HVAC equipment based on the adjusted temperature setpoint.

2. The system of claim 1 , wherein the processing circuit is configured to:

in response to operating the HVAC equipment based on the adjusted temperature setpoint, determine if an additional setpoint adjustment is necessary to drive the HVAC equipment out of the saturation region; and

in response to a determination that the additional setpoint adjustment is necessary, calculate a new value of the adjusted temperature setpoint and operate the HVAC equipment based on the new value of the adjusted temperature setpoint.

3. The system of claim 1 , wherein the system comprises a user interface, wherein the processing circuit is configured to:

receive a temperature adjustment from the user interface; and

determine a temperature constraint based on the temperature adjustment, wherein the temperature setpoint adheres to the temperature constraint.

4. The system of claim 1 , wherein the processing circuit is configured to:

generate a predictive model based on the training data to predict one or more system dynamics; and

perform an optimization based on the predictive model to determine an operating setpoint of the HVAC equipment.

5. The system of claim 1 , wherein the training data indicate one or more system dynamics comprising at least one of:

temperature dynamics of the zone;

building mass temperature dynamics of the building; or

heat transfer dynamics of the HVAC equipment.

6. The system of claim 1 , wherein the processing circuit is configured to determine if the HVAC equipment is in the saturation region by:

determining whether the temperature tracking error exceeds a threshold value for a predetermined period of time; and

determining whether the HVAC equipment is operating in a low range around a minimum load or is operating in a high range around a maximum load for the predetermined period of time.

7. The system of claim 1 , wherein the temperature setpoint is determined based on a pseudorandom binary signal or a random Gaussian signal.

8. A method for exciting system dynamics of a building, the method comprising:

operating heating, ventilation, or air conditioning (HVAC) equipment based on a temperature setpoint to affect a temperature of a zone of the building;

gathering training data indicating the temperature of the zone and the temperature setpoint;

monitoring a temperature tracking error of the zone and a heat transfer value of the HVAC equipment, the temperature tracking error based on the training data;

determining if the HVAC equipment is in a saturation region based on the temperature tracking error and the heat transfer value;

in response to a determination that the HVAC equipment is in the saturation region, calculating an adjusted temperature setpoint; and

operating the HVAC equipment based on the adjusted temperature setpoint.

9. The method of claim 8 , further comprising:

in response to operating the HVAC equipment based on the adjusted temperature setpoint, determining if an additional setpoint adjustment is necessary to drive the HVAC equipment out of the saturation region; and

in response to a determination that the additional setpoint adjustment is necessary, calculating a new value of the adjusted temperature setpoint and operate the HVAC equipment based on the new value of the adjusted temperature setpoint.

10. The method of claim 8 , further comprising:

receiving a temperature adjustment; and

determining a temperature constraint based on the temperature adjustment, wherein the temperature setpoint adheres to the temperature constraint.

11. The method of claim 8 , further comprising:

generating a predictive model based on the training data to predict one or more system dynamics; and

performing an optimization based on the predictive model to determine an operating setpoint of the HVAC equipment.

12. The method of claim 8 , wherein the training data indicate one or more system dynamics comprising at least one of:

temperature dynamics of the zone;

building mass temperature dynamics of the building; or

heat transfer dynamics of the HVAC equipment.

13. The method of claim 8 , wherein determining if the HVAC equipment is in the saturation region comprises:

determining whether the temperature tracking error exceeds a threshold value for a predetermined period of time; and

determining whether the HVAC equipment is operating in a low range around a minimum load or is operating in a high range around a maximum load for the predetermined period of time.

14. A controller for operating for operating heating, ventilation, or air conditioning (HVAC) equipment of a building, the controller comprising:

one or more processors; and

one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

operating the HVAC equipment based on a temperature setpoint to affect a temperature of a zone of the building;

gathering training data indicating the temperature of the zone and the temperature setpoint;

monitoring a temperature tracking error of the zone and a heat transfer value of the HVAC equipment, the temperature tracking error based on the training data;

determining if the HVAC equipment is in a saturation region based on the temperature tracking error and the heat transfer value;

in response to a determination that the HVAC equipment is in the saturation region, calculating an adjusted temperature setpoint; and

operating the HVAC equipment based on the adjusted temperature setpoint.

15. The controller of claim 14 , wherein the operations comprise:

in response to operating the HVAC equipment based on the adjusted temperature setpoint, determining if an additional setpoint adjustment is necessary to drive the HVAC equipment out of the saturation region; and

in response to a determination that the additional setpoint adjustment is necessary, calculating a new value of the adjusted temperature setpoint and operate the HVAC equipment based on the new value of the adjusted temperature setpoint.

16. The controller of claim 14 , wherein the operations comprise:

receiving a temperature adjustment; and

determining a temperature constraint based on the temperature adjustment, wherein the temperature setpoint adheres to the temperature constraint.

17. The controller of claim 14 , wherein the operations comprise:

generating a predictive model based on the training data to predict one or more system dynamics; and

performing an optimization based on the predictive model to determine an operating setpoint of the HVAC equipment.

18. The controller of claim 14 , wherein the training data indicate one or more system dynamics comprising at least one of:

temperature dynamics of the zone;

building mass temperature dynamics of the building; or

heat transfer dynamics of the HVAC equipment.

19. The controller of claim 14 , wherein determining if the HVAC equipment is in the saturation region comprises:

determining whether the temperature tracking error exceeds a threshold value for a predetermined period of time; and

determining whether the HVAC equipment is operating in a low range around a minimum load or is operating in a high range around a maximum load for the predetermined period of time.

20. The controller of claim 14 , wherein the temperature setpoint is determined based on a pseudorandom binary signal or a random Gaussian signal.