ADAPTIVELY LEARNING SURROGATE MODEL FOR PREDICTING BUILDING SYSTEM DYNAMICS FROM SYSTEM IDENTIFICATION MODEL

摘要

Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a system identification model are disclosed herein. The system identification model is used to generate predicted system parameters of a zone of the building based on historic data from operation of the building equipment. The surrogate model is trained based on the predicted system parameters from the system identification model. Predicted future parameters of the variable state of the building are generated using the surrogate model. The surrogate model is re-trained based on new operational data from the building equipment. An updated series of predicted future parameters is generated using the re-trained surrogate model.