SYSTEMS AND METHODS FOR CONTROLLING VARIABLE REFRIGERANT FLOW SYSTEMS AND EQUIPMENT USING ARTIFICIAL INTELLIGENCE MODELS

1 - 40 . (canceled)

41 . A controller for predicting faults in a heating, ventilation, or air conditioning (HVAC) system, the controller comprising a processing circuit configured to:

analyze operating data for the HVAC system using a machine learning model to predict a fault classification for the HVAC system, the fault classification identifying a fault condition affecting the HVAC system;

identify a HVAC device of the HVAC system associated with the fault condition; and

automatically initiate a corrective action to address the fault condition responsive to identifying the HVAC device and the fault condition.

42 . The controller of claim 41 , wherein:

the fault classification includes a severity metric associated with the fault condition, the severity metric indicating a degree of influence that the fault condition has on the HVAC system; and

the corrective action is determined based on both the fault condition and the severity metric associated with the fault condition.

43 . The controller of claim 42 , wherein the processing circuit is configured to:

automatically initiate a first corrective action in response to a value of the severity metric being below a severity threshold; and

automatically initiate a second corrective action in response to the value of the severity metric being above the severity threshold.

44 . The controller of claim 43 , wherein:

the first corrective action comprises providing a notification to a user device; and

the second corrective action comprises scheduling maintenance for the HVAC system or replacement of the HVAC device associated with the fault condition.

45 . The controller of claim 42 , wherein the corrective action comprises taking no action in response to a value of the severity metric being below a severity threshold.

46 . The controller of claim 41 , wherein

the machine learning model is a recurrent neural network (RNN) model; and

analyzing the operating data comprises providing a time series of values of the operating data as an input to the RNN model and obtaining a prediction of the fault classification as an output of the RNN model.

47 . The controller of claim 41 , the processing circuit further configured to generate the machine learning model using a set of simulated training data obtained from a simulation model of the HVAC system.

48 . The controller of claim 41 , wherein the fault classification identifies a plurality of fault conditions affecting the HVAC system, the plurality of fault conditions associated with a plurality of HVAC devices of the HVAC system.

49 . The controller of claim 41 , wherein the fault condition comprises at least one of:

leakage of a refrigerant;

frosting of an outdoor unit;

clogging of an indoor fan;

clogging of an indoor filter;

clogging of a heat exchanger;

clogging of an outdoor fan;

demagnetization of a motor; or

leakage of oil from a compressor.

50 . The controller of claim 41 , wherein the machine learning model is a first machine learning model and the processing circuit is configured to:

use the first machine learning model to predict the fault classification for the HVAC system; and

use a second machine learning model to predict a severity of the fault condition identified by the fault classification.

51 . A method for predicting faults in a heating, ventilation, or air conditioning (HVAC) system, the method comprising:

analyzing operating data for the HVAC system using a machine learning model to predict a fault classification for the HVAC system, the fault classification identifying a fault condition affecting the HVAC system;

identifying a HVAC device of the HVAC system associated with the fault condition; and

automatically initiating a corrective action to address the fault condition responsive to identifying the HVAC device and the fault condition.

52 . The method of claim 51 , wherein:

the fault classification includes a severity metric associated with the fault condition, the severity metric indicating a degree of influence that the fault condition has on the HVAC system; and

the corrective action is determined based on both the fault condition and the severity metric associated with the fault condition.

53 . The method of claim 52 , comprising:

automatically initiating a first corrective action in response to a value of the severity metric being below a severity threshold; and

automatically initiating a second corrective action in response to the value of the severity metric being above the severity threshold.

54 . The method of claim 53 , wherein:

the first corrective action comprises providing a notification to a user device; and

the second corrective action comprises scheduling maintenance for the HVAC system or replacement of the HVAC device associated with the fault condition.

55 . The method of claim 52 , wherein the corrective action comprises taking no action in response to a value of the severity metric being below a severity threshold.

56 . The method of claim 51 , wherein

the machine learning model is a recurrent neural network (RNN) model; and

analyzing the operating data comprises providing a time series of values of the operating data as an input to the RNN model and obtaining a prediction of the fault classification as an output of the RNN model.

57 . The method of claim 51 , comprising generating the machine learning model using a set of simulated training data obtained from a simulation model of the HVAC system.

58 . The method of claim 51 , wherein the fault classification identifies a plurality of fault conditions affecting the HVAC system, the plurality of fault conditions associated with a plurality of HVAC devices of the HVAC system.

59 . The method of claim 51 , wherein the fault condition comprises at least one of:

leakage of a refrigerant;

frosting of an outdoor unit;

clogging of an indoor fan;

clogging of an indoor filter;

clogging of a heat exchanger;

clogging of an outdoor fan;

demagnetization of a motor; or

leakage of oil from a compressor.

60 . The method of claim 51 , wherein the machine learning model is a first machine learning model and the method comprises:

using the first machine learning model to predict the fault classification for the HVAC system; and

using a second machine learning model to predict a severity of the fault condition identified by the fault classification.