SYSTEMS AND METHODS FOR LEARNING AND UTILIZING OCCUPANT TOLERANCE IN DEMAND RESPONSE

1 . A method, comprising:

updating, by one or more processing circuits, a building condition of an HVAC system of a space in a building at a time t 1 , wherein the building condition is updated from a default building condition;

updating, by the one or more processing circuits, the building condition of the HVAC system of the space in the building at a time t 2 ;

receiving, by the one or more processing circuits from a control device, an occupant response of an occupant from the space corresponding with the building condition;

updating, by the one or more processing circuits, an artificial intelligence (AI) model based on the occupant response; and

generating, by the one or more processing circuits using the AI model, one or more actions for the HVAC system of a plurality of spaces of the building.

2 . The method of claim 1 , wherein the one or more actions comprise at least one of updating an operating parameter of the HVAC system for at least a space of the plurality of spaces, updating an operating condition of the HVAC system for at least a space of the plurality of spaces, updating an occupancy schedule of the building.

3 . The method of claim 1 , wherein the AI model comprises a generative large language model (LLM), and wherein the generative LLM comprises a pretrained generative transformer model.

4 . The method of claim 1 , wherein the building condition comprises at least one of a temperature setpoint of the space, a level of lighting in the space, an air quality metric of the space, ventilation of the space, a humidity setpoint of the space, an outdoor air fraction of the space.

5 . The method of claim 1 , wherein the occupant response received from the control device is received from at least one of a thermostat in the space or an application on a mobile device.

6 . The method of claim 5 , further comprising:

in response to receiving the occupant response, presenting, by the one or more processing circuits via a generative AI model on the control device, a prompt corresponding to the building condition;

receiving, by the one or more processing circuits via the generative AI model on the control device, an acceptance of the building condition; and

maintaining, by the one or more processing circuits, the building condition of the HVAC system of the space in the building at a time t 3 .

7 . The method of claim 6 , further comprising:

prompting, by the one or more processing circuits via the generative AI model on the mobile device, the occupant to reduce a building load comprising one or more recommendations to reduce the building load.

8 . The method of claim 1 , further comprising:

collecting or receiving, by the one or more processing circuits, a plurality of unstructured data corresponding to a plurality of occupant responses associated with one or more building conditions of the plurality of spaces in the building; and

training, by the one or more processing circuits, the AI model using the plurality of unstructured data, wherein updating the AI model comprises retraining the AI model based on the occupant response.

9 . The method of claim 8 , wherein the plurality of occupant responses comprises a plurality of tolerance responses of the plurality of occupants of the building associated with a setpoint of at least one of the plurality of spaces of the building.

10 . The method of claim 1 , further comprising:

in response to receiving the occupant response, generating, by the one or more processing circuits using the AI model, one or more actions for the HVAC system corresponding with updating the building condition.

11 . The method of claim 1 , further comprising:

updating, by the one or more processing circuits, a second building condition of the HVAC system of a second space in the building at a time t 4 , wherein the second building condition is updated from the default building condition;

updating, by the one or more processing circuits, the second building condition of the HVAC system of the space in the building at a time t 5 ;

updating, by the one or more processing circuits, the second building condition of an HVAC system of the space in the building at a time t 6 ;

receiving, by the one or more processing circuits from a control device, a second occupant response of a second occupant from the second space corresponding with the second building condition; and

updating, by the one or more processing circuits, the AI model based on the second occupant response.

12 . A building system of a building, the building system comprising one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

update a building condition of an HVAC system of a space in a building at a time t 1 , wherein the building condition is updated from a default building condition;

update the building condition of the HVAC system of the space in the building at a time t 2 ;

receive, from a control device, an occupant response of an occupant from the space corresponding with the building condition;

update an artificial intelligence (AI) model based on the occupant response; and

generate, using the AI model, one or more actions for the HVAC system of a plurality of spaces of the building.

13 . The building system of claim 12 , wherein the one or more actions comprise at least one of updating an operating parameter of the HVAC system for at least a space of the plurality of spaces, updating an operating condition of the HVAC system for at least a space of the plurality of spaces, updating an occupancy schedule of the building.

14 . The building system of claim 12 , wherein the AI model comprises a generative large language model (LLM), and wherein the generative LLM comprises a pretrained generative transformer model.

15 . The building system of claim 12 , wherein the building condition comprises at least one of a temperature setpoint of the space, a level of lighting in the space, an air quality metric of the space, ventilation of the space, a humidity setpoint of the space, an outdoor air fraction of the space.

16 . The building system of claim 12 , wherein the occupant response received from the control device is received from at least one of a thermostat in the space or an application on a mobile device.

17 . The building system of claim 16 , wherein the instructions when executed by the one or more processors, cause the one or more processors to:

in response to receiving the occupant response, present, via a generative AI model on the control device, a prompt corresponding to the building condition;

receive, via the generative AI model on the control device, an acceptance of the building condition; and

maintain the building condition of the HVAC system of the space in the building at a time t 3 .

18 . The building system of claim 17 , wherein the instructions when executed by the one or more processors, cause the one or more processors to:

prompt, via the generative AI model on the mobile device, the occupant to reduce a building load comprising one or more recommendations to reduce the building load.

19 . The building system of claim 12 , wherein the instructions when executed by the one or more processors, cause the one or more processors to:

collect or receive a plurality of unstructured data corresponding to a plurality of occupant responses associated with one or more building conditions of the plurality of spaces in the building; and

train the AI model using the plurality of unstructured data, wherein updating the AI model comprises retraining the AI model based on the occupant response.

20 . A non-transitory computer readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

update a building condition of an HVAC system of a space in a building at a time t 1 , wherein the building condition is updated from a default building condition;

update the building condition of the HVAC system of the space in the building at a time t 2 ;

receive, from a control device, an occupant response of an occupant from the space corresponding with the building condition;

update an artificial intelligence (AI) model based on the occupant response; and

generate, using the AI model, one or more actions for the HVAC system of a plurality of spaces of the building.