Patents by Inventor Michael Risbeck

Michael Risbeck has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Adaptively learning surrogate model for predicting building system dynamics from simulation model
    Patent number: 11531308
    Abstract: Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a simulation model are disclosed herein. The simulation model is calibrated for a building of interest. The building of interest includes building equipment operable to control a variable state of the building. The simulated data of system states are generated using the calibrated simulation model. A surrogate model is trained based on the simulated data of system states from the calibrated simulation model. System state predictions are generated using the surrogate model. The surrogate model is re-trained based on updated operational data. An updated series of system state predictions is generated using the re-trained surrogate model.
    Type: Grant
    Filed: December 23, 2019
    Date of Patent: December 20, 2022
    Assignee: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
    Inventors: Young M. Lee, Zhanhong Jiang, Kirk Drees, Michael Risbeck
  • Adaptively learning surrogate model for predicting building system dynamics from system identification model
    Patent number: 11409250
    Abstract: 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.
    Type: Grant
    Filed: December 23, 2019
    Date of Patent: August 9, 2022
    Assignee: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
    Inventors: Young M. Lee, Zhanhong Jiang, Kirk Drees, Michael Risbeck
  • ADAPTIVELY LEARNING SURROGATE MODEL FOR PREDICTING BUILDING SYSTEM DYNAMICS FROM SYSTEM IDENTIFICATION MODEL
    Publication number: 20210191348
    Abstract: 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.
    Type: Application
    Filed: December 23, 2019
    Publication date: June 24, 2021
    Applicant: Johnson Controls Technology Company
    Inventors: Young M. Lee, Zhanhong Jiang, Kirk Drees, Michael Risbeck
  • ADAPTIVELY LEARNING SURROGATE MODEL FOR PREDICTING BUILDING SYSTEM DYNAMICS FROM SIMULATION MODEL
    Publication number: 20210191343
    Abstract: Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a simulation model are disclosed herein. The simulation model is calibrated for a building of interest. The building of interest includes building equipment operable to control a variable state of the building. The simulated data of system states are generated using the calibrated simulation model. A surrogate model is trained based on the simulated data of system states from the calibrated simulation model. System state predictions are generated using the surrogate model. The surrogate model is re-trained based on updated operational data. An updated series of system state predictions is generated using the re-trained surrogate model.
    Type: Application
    Filed: December 23, 2019
    Publication date: June 24, 2021
    Applicant: Johnson Controls Technology Company
    Inventors: Young M. Lee, Zhanhong Jiang, Kirk Drees, Michael Risbeck