Augmented deep learning using combined regression and artificial neural network modeling

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

creating a regression model using pre-operation 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 neural network model using the operational data;

determining, independent of the first artificial neural network model, whether the operational data satisfies a first sufficiency threshold;

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 the first sufficiency threshold.

2. The method of claim 1 , further comprising creating a second artificial neural network model using the operational data; and

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

3. The method of claim 1 , wherein the first sufficiency threshold is satisfied when at least a threshold quantity of the operational data is collected.

4. The method of claim 1 , wherein transitioning from using the regression model to using the first artificial neural network model is further responsive to satisfying a criterion indicative of similarity between the operational data and new operational data.

5. The method of claim 4 , 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.

6. The method of claim 1 , wherein transitioning from using the regression model and using the first artificial neural network model comprises calculating a combined output using both the regression model and the first artificial neural network model and using the combined output to perform the model-driven operations.

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

creating a regression model using pre-operation 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;

prior to initiating training of a first artificial neural network model, determining whether a first amount of operational data has been collected;

training the first artificial neural network model using the operational data responsive to determining that the first amount of the operational data has been collected; and

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

8. The method of claim 7 , 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.

9. The method of claim 8 , 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.

10. The method of claim 7 , 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.

11. The method of claim 7 , 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.

12. The method of claim 11 , 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.

13. The method of claim 7 , 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.

14. The method of claim 13 , wherein the system comprises HVAC equipment.