System identification and model development

摘要

Methods for system identification are presented using model predictive control to frame a gray-box parameterized state space model. System parameters are identified using an optimization procedure to minimize a first error cost function within a range of filtered training data. Disturbances are accounted for using an implicit integrator within the system model, as well as a parameterized Kalman gain. Kalman gain parameters are identified using an optimization procedure to minimize a second error cost function within a range of non-filtered training data. Recursive identification methods are presented to provide model adaptability using an extended Kalman filter to estimate model parameters and a Kalman gain to estimate system states.

主权项

1. A computer-implemented method for identifying model parameters including system parameters and Kalman gain parameters in a dynamic model of a building system, the method comprising:
receiving, at a controller for the building system, training data including input data and output data, wherein the input data includes a setpoint applied as a controlled input to the building system, wherein the output data measures a state or linear combination of states of the building system in response to both the setpoint and an extraneous disturbance, wherein the extraneous disturbance comprises an uncontrolled thermal input to the building system that affects the state or linear combination of states measured by the output data; performing, by the controller, a two-stage optimization process to identify the system parameters and the Kalman gain parameters, the two-stage optimization process comprising:
a first stage in which the controller filters the training data to remove an effect of the extraneous disturbance from the output data and uses the filtered training data to identify the system parameters, wherein the system parameters describe energy transfer characteristics of the building system, anda second stage in which the controller uses the non-filtered training data to identify the Kalman gain parameters, wherein the Kalman gain parameters account for the extraneous disturbance; and using the dynamic model to generate, by the controller, a new setpoint for the building system, wherein the building system uses the new setpoint to affect the state or linear combination of states measured by the output data.