Heuristic method of automated and learning control, and building automation systems thereof
Apparatuses, systems, and methods of physical-model based building automation using in-situ regression to optimize control systems are presented. A simulation engine is configured to simulate a behavior or a controlled system using a physical model for the controlled system. A data stream comprises data from a controlled system. A training loop is configured to compare an output of a simulation engine to a data stream using a heuristic so that a physical model is regressed in a manner that the output of the simulation engine approaches the data stream.
1. A controller for adjusting a model of a controlled system, the controller comprising:
a memory; and
a processor in communication with the memory and configured to:
receive a data stream from the controlled system;
simulate a behavior of the controlled system using a physical model of the controlled system to produce a model output,
compare the model output to the data stream producing a difference between the model output and the data stream, and
use the difference between the model output and the data stream to adjust the physical model.
2. The controller of claim 1 , wherein when the model output approaches the data stream sufficiently closely, using the physical model to predict a future value of the data stream.
3. The controller of claim 2 , wherein when the model output approaches the data stream sufficiently closely step comprises at least one of: finding a reduction in error between values of the data stream and values of the model output to within an arbitrary threshold; finding a reduction in uncertainty between values of the data stream and values of the model output; finding a reduction in uncertainty of values of the model output to within an arbitrary threshold; reaching an arbitrary threshold on number of erroneous values of the model output; reaching an arbitrary threshold on number of accurate values of the model output; reaching an arbitrary threshold on number of values in the model output; and reaching an arbitrary threshold on computational time spent.
4. The controller of claim 1 , wherein the data stream comprises sensor measurements, equipment state, environmental data, occupant input, or occupant behavior.
5. The controller of claim 1 , wherein adjusting the physical model comprises adjusting parameters of the physical model or modifying inputs of the physical model.
6. The controller of claim 1 , wherein the compare the model output to the data stream comprises using a cost function.
7. The controller of claim 6 , wherein the cost function is time variant.
8. The controller of claim 1 , wherein the adjust the physical model comprises regressing the physical model using a difference of the model output and the data stream to determine at least one adjustment to the physical model.
9. The controller of claim 8 , wherein regressing the physical model comprises regressing the physical model using differential comparison.
10. A method executed by at least one processor for modifying a physical model of a controlled system, the method comprising:
receiving, by the at least one processor, a data stream from the controlled system;
simulating a behavior of the controlled system using a physical model of the controlled system to produce a model output,
comparing the model output to the data stream producing a difference between the model output and the data stream, and
using the difference between the model output and the data stream to adjust the physical model.
11. The method of claim 10 , wherein the model output that approaches the data stream sufficiently closely step comprises one or more of finding a reduction in error between values of the data stream and values of the model output to within an arbitrary threshold; finding a reduction in uncertainty between values of the data stream and values of the model output to within an arbitrary threshold; finding a reduction in uncertainty of values of the model output to within an arbitrary threshold; reaching an arbitrary threshold on number of erroneous values of the model output; reaching an arbitrary threshold on number of accurate values of the model output; reaching an arbitrary threshold on number of values in the model output; and reaching an arbitrary threshold on computational time spent.
12. The method of claim 10 , wherein the data stream comprises sensor measurements, equipment state, environmental data, occupant input, or occupant behavior.
13. The method of claim 10 , wherein when the model output approaches the data stream sufficiently closely, using output of the physical model predict the data stream into a future.
14. The method of claim 10 , further comprising using a cost function to evaluate difference between the data stream and the model output.
15. The method of claim 14 , wherein the cost function is time variant.
16. The method of claim 10 wherein adjusting the physical model comprises making a modification to model inputs or making a modification to model parameters.
17. A non-transitory machine-readable medium encoded with instructions for execution by a processor for modifying a physical model of a controlled system, the non-transitory machine-readable medium comprising:
instructions for receiving, by the processor, a data stream from the controlled system;
instructions for simulating a behavior of the controlled system using a physical model of the controlled system to produce a model output,
instructions for comparing the model output to the data stream producing a difference between the model output and the data stream, and
instructions for using the difference between the model output and the data stream to adjust the physical model.
18. The non-transitory machine-readable medium of claim 17 , further comprising instructions for using the physical model to predict a future value of the data stream when the model output approaches the data stream sufficiently closely.
19. The non-transitory machine-readable medium of claim 17 , wherein the data stream comprises sensor measurements, equipment state, environmental data, occupant input, or occupant behavior.
20. The non-transitory machine-readable medium of claim 17 , further comprising further comprising instructions for using a cost function to evaluate difference between the data stream and the model output.