AUGMENTED DEEP LEARNING USING COMBINED REGRESSION AND ARTIFICIAL NEURAL NETWORK MODELING

1 - 20 . (canceled)

21 . A method for operating a building management system for a physical plant, the method comprising:

creating a model using simulated or generated plant data during a pre-operational stage of physical plant;

identifying a plurality of data groups generated by physical plant data during an operational stage of the physical plant;

determining whether the plurality of data groups exceeds a first data sufficiency threshold;

in response to a determination that the plurality of data groups exceeds the first data sufficiency threshold, creating a first artificial neural network model using the plurality of data groups;

determining whether new physical plant data meets a first similarity criterion of at least one of the plurality of data groups;

in response to a determination that the new physical plant data meets the first similarity criterion, making a first artificial neural network prediction using the first artificial neural network model; and

modifying a characteristic of the physical plant according to the first artificial neural network prediction.

22 . The method of claim 21 , wherein the simulated or generated plant data comprises at least one of manufacturing data and offsite data.

23 . The method of claim 21 , wherein the physical plant data comprises at least one of plant input data or plant output data, wherein the data groups are data clusters and wherein the model is a linear regression model.

24 . The method of claim 21 , wherein the first data sufficiency threshold is based on a quantity of physical plant data.

25 . The method of claim 21 , wherein the method further comprises:

in response to a determination that the plurality of data groups does not exceed the first data sufficiency threshold, making a regression model prediction using the regression model;

utilizing the regression model prediction to perform at least one of a fault detection task, a fault diagnosis task, or a control task.

26 . The method of claim 21 , wherein the method further comprises utilizing the first artificial neural network prediction as an input to the regression model, the first artificial neural network prediction configured to improve a quality of the regression model.

27 . A method for initiating and automatically improving model-driven operations in a low-data scenario, the method comprising:

creating a regression model using simulated or generated plant data prior to initiating the model-driven operations;

using the regression model to initiate and perform the model-driven operations during an operational stage;

collecting operational data during the operational stage;

creating a first artificial intelligence model using the operational data;

transitioning from using the regression model to perform the model-driven operations to using the first artificial neural network model to perform the model-driven operations responsive to the operational data satisfying a first sufficiency threshold.

28 . The method of claim 27 , further comprising creating a second artificial intelligence model using the operational data; and

transitioning from using the first artificial intelligence model to perform the model-driven operations to using the second artificial model to perform the model-driven operations responsive to the operational data satisfying a second sufficiency threshold.

29 . The method of claim 27 , wherein the first sufficiency threshold is satisfied when at least a threshold quantity of the operational data is collected.

30 . The method of claim 27 , wherein transitioning from using the regression model to using the first artificial intelligence model is further responsive to satisfying a criterion indicative of similarity between the operational data and new operational data.

31 . The method of claim 30 , wherein the criterion indicative of similarity between the operational data and the new operational data is based on a distance between the new operational data and a cluster of the operational data.

32 . The method of claim 27 , wherein transitioning from using the regression model and using the first artificial intelligence model comprises calculating a combined output using both the regression model and the first artificial artificial intelligence model and using the combined output to perform the model-driven operations, wherein the first artificial intelligence model is a neural network model.

33 . A method for initiating and automatically improving model-driven operations in a low-data scenario, the method comprising:

creating a regression model using simulated or generated plant data prior to initiating the model-driven operations;

using the regression model to initiate and perform the model-driven operations during an operational stage;

creating a first artificial neural network model using operational data associated with the model driven operations responsive to determining that a first amount of the operational data bring available; and

using the first artificial neural network model to continue performing the model-driven operations during the operational stage.

34 . The method of claim 33 , wherein using the first neural network model to continue performing the model-driven operations during the operational stage comprises combining outputs of the first artificial neural network model and the regression model to generate a combined output and using the combined output to perform the model-driven operations.

35 . The method of claim 34 , wherein combining the outputs of the first artificial neural network model and the regression model comprises calculating a weighted average, wherein the weighted average is weighted based on a distance between a new data sample of the operational data and a cluster of previous data samples of the operational data.

36 . The method of claim 33 , wherein using the first neural network model to continue performing the model-driven operations during the operational stage comprises determining whether to use the first artificial neural network model or the regression model based on a quantification of similarity between a new data sample of the operational data and one or more previous samples of the operational data.

37 . The method of claim 33 , wherein performing the model-driven operations during the operational stage comprises collecting more of the operational data, the method further comprising:

creating a second artificial neural network model using the operational data responsive to determining that a second amount of the operational data has been collected; and

using the second artificial neural network model to continue performing the model-driven operations during the operational stage.

38 . The method of claim 37 , wherein using the second artificial neural network model to continue performing the model-driven operations during the operational stage comprises combining outputs of the first artificial neural network model and the second artificial neural network model to generate a combined output and using the combined output to perform the model-driven operations.

39 . The method of claim 33 , wherein performing the model-driven operations during the operational stage comprises controlling a system using a prediction of the regression model, the first artificial neural network model, or a combination of the regression model and the first artificial neural network model.

40 . The method of claim 39 , wherein the system comprises HVAC equipment.