Efficient integration of machine learning models in building management systems

1. A building management system (BMS) comprising:

one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to:

generate, on a remote computing system, a first predictive model using one or more machine learning techniques, wherein the first predictive model is generated using a first operating data set in response to determining a size of the first operating data set exceeds a threshold, the first operating data set collected at a first time interval based on one or more operating parameters associated with at least one of a plurality of BMS subsystems;

store the first predictive model at the first time interval in the remote computing system in an unbuilt state;

receive a prediction request from a user input at a second time interval following the first time interval;

retrieve the first predictive model in the unbuilt state from the remote computing system;

rebuild, locally, the first predictive model;

execute the rebuilt predictive model to generate a first prediction in response to the prediction request;

initiate an automated response based on the first prediction, wherein initiating the automated response comprises performing an automated control action comprising at least one of (i) controlling equipment to enter a safety mode, (ii) operating the equipment at a derated state, (iii) shutting down the equipment, (iv) controlling the equipment using an adjusted setpoint, (v) controlling the equipment to work-around a fault, or (vi) controlling the equipment to prevent the fault from occurring; and

automatically train, at an end of an update period beginning when the first predictive model is generated, the first predictive model using the first operating data set and a second operating data set collected at a fourth time interval, wherein the fourth interval time is after the first interval time, wherein the update period is based on the size of the first operating data set.

2. The BMS of claim 1 , where in the BMS subsystems are virtual subsystems and the first operating data set is a virtual operating data set.

3. The BMS of claim 1 , wherein the instructions cause the one or more processors to:

in response to the prediction request, receive a current data set; and

generate the first prediction using the first predictive model based on the prediction request and the current data set.

4. The BMS of claim 1 , the instructions further comprising:

determining a diagnosis associated with the first prediction to facilitate taking preventative measures; and

performing the automated control action based on the diagnosis to prevent a predicted fault from occurring.

5. The BMS of claim 1 , wherein the first prediction is a fault prediction for at least one of a plurality of the BMS subsystems.

6. The BMS of claim 1 , wherein generating the first predictive model comprises:

transmitting the first operating data set based on the one or more operating parameters associated with at least one of a plurality of BMS subsystems to the remote computing system; and

receiving the first predictive model from the remote computing system.

7. The BMS of claim 1 , wherein storing the first predictive model at a first time interval comprises:

transmitting the first predictive model to the remote computing system.

8. The BMS of claim 1 , wherein retrieving the first predictive model comprises:

receiving the first predictive model from the remote computing system.

9. The BMS of claim 1 , wherein initiating an automated response based on the first prediction comprises:

transmitting a notification to a user indicating the first prediction.

10. The BMS of claim 9 , wherein the notification to the user comprises:

generating and displaying a GUI based on the first prediction.

11. The BMS of claim 1 , wherein the first predictive model is associated with at least one of a plurality of types of building equipment, and wherein the update period is further based on the at least one of the plurality of types of building equipment.

12. A method of controlling building equipment, comprising:

generating, on a remote computing system, a first predictive model using one or more machine learning techniques, wherein the first predictive model is generated using a first operating data set in response to determining a size of the first operating data set exceeds a threshold, the first operating data set collected at a first time interval based on one or more operating parameters associated with the building equipment;

storing the first predictive model at the first time interval in the remote computing system;

receiving a prediction request from a user input at a second time interval following the first time interval;

retrieving the first predictive model from the remote computing system;

rebuilding, in a local computing system, the first predictive model;

executing the rebuilt predictive model to generate a first prediction in response to the prediction request;

initiating an automated response based on the first prediction, wherein initiating the automated response comprises performing an automated control action comprising at least one of (i) controlling equipment to enter a safety mode, (ii) operating the equipment at a derated state, (iii) shutting down the equipment, (iv) controlling the equipment using an adjusted setpoint, (v) controlling the equipment to work-around a fault, or (vi) controlling the equipment to prevent the fault from occurring; and

automatically training, at an end of an update period beginning when the first predictive model is generated, the first predictive model using the first operating data set and a second operating data set collected at a fourth time interval, wherein the fourth interval time is after the first interval time, wherein the update period is based on the size of the first operating data set.

13. The method of claim 12 , further comprising:

in response to the prediction request, receiving a current data set; and

generating the first prediction using the first predictive model based on the prediction request and the current data set.

14. The method of claim 12 further comprising:

determining a diagnosis associated with the first prediction to facilitate taking preventative measures; and

performing the automated control action based on the diagnosis to prevent a predicted fault from occurring.

15. The method of claim 12 , wherein the first predictive model is associated with at least one of a plurality of types of building equipment, and wherein the update period is further based on the at least one of the plurality of types of building equipment.

16. A building management system (BMS) configured to:

generate, on a remote computing system, a first predictive model using one or more machine learning techniques, wherein the first predictive model is generated using a first operating data set in response to determining a size of the first operating data set exceeds a threshold, the first operating data set collected at a first time interval based on one or more operating parameters associated with HVAC equipment;

store the first predictive model at the first time interval in the remote computing system;

receive a prediction request from a user input at a second time interval following the first time interval;

retrieve the first predictive model from the remote computing system;

rebuild, locally, the first predictive model;

execute the rebuilt predictive model to generate a first prediction in response to the prediction request;

initiate an automated control response based on the first prediction, wherein initiating the automated response comprises performing an automated control action comprising at least one of (i) controlling equipment to enter a safety mode, (ii) operating the equipment at a derated state, (iii) shutting down the equipment, (iv) controlling the equipment using an adjusted setpoint, (v) controlling the equipment to work-around a fault, or (vi) controlling the equipment to prevent the fault from occurring; and

automatically train, at an end of an update period beginning when the first predictive model is generated, the first predictive model using the first operating data set and a second operating data set collected at a fourth time interval, wherein the fourth interval time is after the first interval time, wherein the update period is based on the size of the first operating data set.